JAMES - Joint Automatic Measurement and Evaluation System

JAMES 1.8.0 (November 2025)

Author

Stef van Buuren, Arjan Huizing, Iris Eekhout (TNO Child Health)

Overview

JAMES is a web service for creating and interpreting charts of child growth and development. The current version of JAMES

  1. provides access to high-quality growth charts used by the Dutch youth health care;
  2. screens for abnormal height, weight and head circumference;
  3. converts developmental data into the D-score;
  4. predicts future growth and development.

JAMES is a RESTful API that runs on a remote host. The system accepts requests from the Dutch Digital Child Health Record System DD-JGZ and other clients using a the Basisdataset JGZ 4.0.0 protocol. Child data are coded according to the JSON data schema 3.0.

The following sections illustrate how a client can make requests to JAMES using various client languages. In principle, any HTTP client will work with JAMES. The document highlights some applications of the service and provides pointers to relevant background information.

The service aids in monitoring and evaluating childhood growth. JAMES is created and maintained by the Netherlands Organisation for Applied Scientific Research TNO. Please contact Stef van Buuren <stef.vanbuuren at tno.nl> for further information.

Primary JAMES user functionality

Verb API end point Description Maps to james function
POST /version/{dfm} Obtain version information version()
POST /data/upload/{dfm} Upload child data upload_data()
       
POST /charts/draw/{ffm} Draw child data on growth chart draw_chart()
POST /charts/list/{dfm} List available growth charts list_charts()
POST /charts/validate/{dfm} Validate a chart code validate_chartcode()
       
POST /dscore/calculate/{dfm} Calculate developmental score (D-score) calculate_dscore()
POST /vwc/select/{dfm} Select developmental milestones for age select_vwc()
POST /vwc/percentiles/{dfm} Obtain developmental milestone percentiles percentiles_vwc()
POST /dcat/calculate/{dfm} Adaptive testing of milestones dcat()
       
POST /screeners/list/{dfm} List available growth screeners list_screeners()
POST /screeners/apply/{dfm} Apply growth screeners to child data apply_screeners()
       
GET /site Request empty site request_site()
POST /site/request/{dfm} Request personalised site request_site()
       
POST /blend/request/{sfm} Obtain a blend from multiple end points request_blend()
       
GET /{session}/{info} Extract session details
GET /{2}/{1}/man Consult R help help({1}_{2})

The table lists the defined API end points and the mapping to each end point to the corresponding R function.

The definition of the JAMES endpoints can be found at OpenAPI specification.

Output formats

JAMES is built on top of the OpenCPU API, a powerful and flexible way for online deployment of R functionality. Although it is possible to use JAMES without knowledge of OpenCPU, it is useful to browse the OpenCPU features.

OpenCPU offers multiple output formats out of the box. JAMES supports a subset of these through the /{session}/{info} end point. Here session is a special session code generated by OpenCPU that identifies the server location containing the results of a request. The output format info is one of the following:

{info} Description
json Function result as JSON
print Function result as formatted as print
csv Function result as comma-separated format
tab Function result as tab-delimited format
md Function result as markdown format
svglite Function result as SVG graph
warnings Warnings from the R execution
messages Messages, especially data validation info
console Console print out, useful for locating errors
stdout Standard output
info Overview of JAMES deployment
parent Parent directory of all OpenCPU session output

In addition, the user can request the function result in a particular form. JAMES distinguishes the following groups of formats.

Format group Description
dfm Data format: json, csv, tab, md, print, parent
ffm Figure format: svglite, print, parent
sfm System format: json, print, parent

In general, the user can specify the desired format by appending the format name to the URL. See https://www.opencpu.org/api.html#api-formats for examples.

Objective

This document provides a quick introduction into the main JAMES features, and how these can be assessed from R and from the command line.

Features

/version: Obtain version information

Let us first check whether JAMES is running. The following code makes a simple request to JAMES to see whether it is alive and to return the version number of the underlying james R package. We illustrate both requests in R and in bash.

We first need to install and load packages.

install.packages(c("remotes", "httr", "jsonlite"))
remotes::install_github("growthcharts/jamesclient")
remotes::install_github("growthcharts/jamesdemodata")
remotes::install_github("growthcharts/bdsreader")
library(jamesclient)
library(httr)
library(jsonlite)

Let’s find out the JAMES version number. In this document, we define the server that hosts JAMES as follows.

# host <- "http://localhost:8080"
host <- "https://james.groeidiagrammen.nl"
system(paste0("echo ", host, " > .host"))

We first illustrate a method that makes two requests to the server. The following commands call the /version/json end point in the JAMES API.

r <- james_post(host = host, path = "version/json")

We added the /json to the pathname to extract the JSON representation of the result of the R function james::version(). The function result is an object of class james_post and consists of various components.

names(r)
 [1] "url"          "status_code"  "headers"      "all_headers"  "cookies"     
 [6] "content"      "date"         "times"        "request"      "handle"      
[11] "request_path" "parsed"       "warnings"     "messages"     "session"     
r$url
[1] "https://james.groeidiagrammen.nl/version/json"

Most of the element are documented in the response object in the httr package. For example, we could use the call httr::status_code(r) to obtain the status code. The function james_post() adds the last five elements:

  • r$request_path echoes the endpoint, here /version/json;
  • r$parsed is a parsed version of the element r$content. Here it is a list of elements like names of the package, its date, and so on. In case of an error of the server function, we find the error message here;
  • r$warnings contain any warnings thrown during execution;
  • r$messages contain any messages, e.g. data reading errors;
  • r$session (like x0ee521009f2ffc) is a unique session code.

The jamesclient::james_post() function wraps the basis workhorse httr::POST() that does the actual server request. For illustration, we may obtain equivalent content by the POST function directly.

path <- "version/json"
url <- parse_url(host)
url <- modify_url(url, path = file.path(url$path, path), query = "auto_unbox=true")
r <- POST(url)
fromJSON(content(r, type = "text", encoding = "UTF-8"))
$package
[1] "james"

$packageVersion
[1] "1.8.0"

$packageDate
[1] "2025-11-12"

$Rversion
[1] "4.5.0"

We use the curl Linux command. If needed, on Ubuntu install curl as

sudo apt update
sudo apt -y install curl

Let’s find out the JAMES version number. We first illustrate a method that makes two requests to the server.

The following bash commands call the /version API end point

curl -sX 'GET' \
   $(cat .host)/version \
  -H 'accept: text/json'
  
curl -sX POST $(cat .host)/version > resp
Redirect to http://james.groeidiagrammen.nl/ocpu/library/james/R/version/print

The response to the request consists of a set of URLs created on the server, each of which contains details on the response.

cat resp
/ocpu/tmp/x0e7f03da99dd65/R/.val
/ocpu/tmp/x0e7f03da99dd65/R/version
/ocpu/tmp/x0e7f03da99dd65/stdout
/ocpu/tmp/x0e7f03da99dd65/source
/ocpu/tmp/x0e7f03da99dd65/console
/ocpu/tmp/x0e7f03da99dd65/info
/ocpu/tmp/x0e7f03da99dd65/files/DESCRIPTION

The path element following tmp/ is a unique session key. See https://www.opencpu.org/api.html for the interpretation of the OpenCPU API.

The next snippet constructs the URL of a JSON representation of the result and downloads the contents of the URL as a file value1.

curl -s $(cat .host)$(head -1 resp)/json?auto_unbox=true > value1
cat value1
{
  "package": "james",
  "packageVersion": "1.8.0",
  "packageDate": "2025-11-12",
  "Rversion": "4.5.0"
}

The above sequence makes two requests to the server. The following code compacts both steps into one.

curl -sX POST $(cat .host)/version/json?auto_unbox=true > value2
cat value2
{
  "package": "james",
  "packageVersion": "1.8.0",
  "packageDate": "2025-11-12",
  "Rversion": "4.5.0"
}

/data/upload: Upload child data

JAMES understands data that conform to the Basisdataset JGZ 4.0.1 coded as JSON according to a JSON schema. This section explains how we create, validate and upload child data to JAMES.

Let us assume that we have already have child data in R stored as a data.frame or tibble. Here we copy the longitudinal demo data maria.json from the bdsreader package into the working directory.

success <- file.copy(system.file("examples/maria.json", package = "bdsreader"), 
                     "maria.json", overwrite = TRUE)

The contents of the file is json format, ready for upload:

{
  "OrganisatieCode": 1234,
  "Referentie": "fa308134-069e-49ce-9847-ccdae380ed6f",
  "ClientGegevens": {
    "Elementen": [
      {
        "Bdsnummer": 19,
        "Waarde": "2"
      },
      {
        "Bdsnummer": 20,
        "Waarde": "20181011"
      },
      {
        "Bdsnummer": 82,
        "Waarde": "189"
      },
      {
        "Bdsnummer": 91,
        "Waarde": "2"
      },
      {
        "Bdsnummer": 110,
        "Waarde": "990"
      },
      {
        "Bdsnummer": 238,
        "Waarde": "1670"
      },
      {
        "Bdsnummer": 240,
        "Waarde": "1900"
      }
    ],
    "Groepen": [
      {
        "Elementen": [
          {
            "Bdsnummer": 63,
            "Waarde": "19950704"
          },
          {
            "Bdsnummer": 71
          },
          {
            "Bdsnummer": 62,
            "Waarde": "01"
          }
        ]
      },
      {
        "Elementen": [
          {
            "Bdsnummer": 63,
            "Waarde": "19901202"
          },
          {
            "Bdsnummer": 71
          },
          {
            "Bdsnummer": 62,
            "Waarde": "02"
          }
        ]
      }
    ]
  },
  "Contactmomenten": [
    {
      "Tijdstip": "20181011",
      "Elementen": [
        {
          "Bdsnummer": 245,
          "Waarde": "990"
        }
      ]
    },
    {
      "Tijdstip": "20181111",
      "Elementen": [
        {
          "Bdsnummer": 235,
          "Waarde": "380"
        },
        {
          "Bdsnummer": 245,
          "Waarde": "1250"
        },
        {
          "Bdsnummer": 252,
          "Waarde": "270"
        }
      ]
    },
    {
      "Tijdstip": "20181211",
      "Elementen": [
        {
          "Bdsnummer": 235,
          "Waarde": "435"
        },
        {
          "Bdsnummer": 245,
          "Waarde": "2100"
        },
        {
          "Bdsnummer": 252,
          "Waarde": "305"
        }
      ]
    }
  ]
}

There are four ways to upload the data to JAMES:

  1. Upload the file "maria.json";
  2. Convert to a string and upload;
  3. Convert to a JSON object and upload;
  4. Read the JSON file from a URL.

The /data/upload API end point handles these cases as follows:

# upload as file
fn <- "maria.json"
r1 <- james_post(host = host, path = "data/upload/json", txt = fn)
status_code(r1)
[1] 201
# upload as string
js <- read_json_js(fn)
r2 <- james_post(host = host, path = "data/upload/json", txt = js)
status_code(r2)
[1] 201
# upload as JSON object
jo <- read_json_jo(fn)
r3 <- james_post(host = host, path = "data/upload/json", txt = jo)
status_code(r3)
[1] 201
# upload as URL
url <- file.path(host, "ocpu/library/bdsreader/examples/maria.json")
r4 <- james_post(host = host, path = "data/upload/json", txt = url)
status_code(r4)
[1] 201

If the status is 201, the data are uploaded to JAMES and processed. For example, the processed data after file upload is available as an R data frame under element r1$parsed.

r1$parsed
$psn
  id  name        dob       dobm       dobf  src    sex gad ga smo  bw hgtm
1 -1 Maria 2018-10-11 1990-12-02 1995-07-04 1234 female 189 27   1 990  167
  hgtf
1  190

$xyz
      age xname yname zname                  zref       x       y      z
1  0.0849   age   hgt hgt_z nl_2012_hgt_female_27  0.0849 38.0000 -0.158
2  0.1670   age   hgt hgt_z nl_2012_hgt_female_27  0.1670 43.5000  0.047
3  0.0000   age   wgt wgt_z nl_2012_wgt_female_27  0.0000  0.9900  0.190
4  0.0849   age   wgt wgt_z nl_2012_wgt_female_27  0.0849  1.2500 -0.203
5  0.1670   age   wgt wgt_z nl_2012_wgt_female_27  0.1670  2.1000  0.015
6  0.0849   age   hdc hdc_z nl_2012_hdc_female_27  0.0849 27.0000 -0.709
7  0.1670   age   hdc hdc_z nl_2012_hdc_female_27  0.1670 30.5000 -0.913
8  0.0000   age   bmi bmi_z nl_1997_bmi_female_nl  0.0000      NA     NA
9  0.0849   age   bmi bmi_z nl_1997_bmi_female_nl  0.0849  8.6565 -5.719
10 0.1670   age   bmi bmi_z nl_1997_bmi_female_nl  0.1670 11.0979 -3.767
11 0.0000   hgt   wfh wfh_z   nl_2012_wfh_female_      NA  0.9900     NA
12 0.0849   hgt   wfh wfh_z   nl_2012_wfh_female_ 38.0000  1.2500 -0.001
13 0.1670   hgt   wfh wfh_z   nl_2012_wfh_female_ 43.5000  2.1000  0.326

The session details, including the uploaded data, will remain available for a limited time. After 30 minutes the session is wiped. The session key is your entrance to the resource within the 30-minute window. The key can be retrieved as r1$session. For example, to see the result of the file upload session in markdown use

(session <- r1$session)
[1] "x0c7b899f22cfb7"
resp <- james_get(host = host, path = file.path(session, "md"))
cat(resp$parsed)


  * **psn**:

    -------------------------------------------------------------------------------
     id   name       dob          dobm         dobf      src    dnr    sex     gad
    ---- ------- ------------ ------------ ------------ ------ ----- -------- -----
     -1   Maria   2018-10-11   1990-12-02   1995-07-04   1234   NA    female   189
    -------------------------------------------------------------------------------

    Table: Table continues below

    -----------------------------------------------------------------------------------
     ga   smo   bw    hgtm   hgtf   agem   etn   pc4   blbf   blbm   eduf   edum   par
    ---- ----- ----- ------ ------ ------ ----- ----- ------ ------ ------ ------ -----
     27    1    990   167    190     NA    NA    NA     NA     NA     NA     NA    NA
    -----------------------------------------------------------------------------------

  * **xyz**:

    ----------------------------------------------------------------------------------
      age     xname   yname   zname           zref              x        y       z
    -------- ------- ------- ------- ----------------------- -------- ------- --------
     0.0849    age     hgt    hgt_z   nl_2012_hgt_female_27   0.0849    38     -0.158

     0.167     age     hgt    hgt_z   nl_2012_hgt_female_27   0.167    43.5    0.047

       0       age     wgt    wgt_z   nl_2012_wgt_female_27     0      0.99     0.19

     0.0849    age     wgt    wgt_z   nl_2012_wgt_female_27   0.0849   1.25    -0.203

     0.167     age     wgt    wgt_z   nl_2012_wgt_female_27   0.167     2.1    0.015

     0.0849    age     hdc    hdc_z   nl_2012_hdc_female_27   0.0849    27     -0.709

     0.167     age     hdc    hdc_z   nl_2012_hdc_female_27   0.167    30.5    -0.913

       0       age     bmi    bmi_z   nl_1997_bmi_female_nl     0       NA       NA

     0.0849    age     bmi    bmi_z   nl_1997_bmi_female_nl   0.0849   8.657   -5.719

     0.167     age     bmi    bmi_z   nl_1997_bmi_female_nl   0.167    11.1    -3.767

       0       hgt     wfh    wfh_z    nl_2012_wfh_female_      NA     0.99      NA

     0.0849    hgt     wfh    wfh_z    nl_2012_wfh_female_      38     1.25    -0.001

     0.167     hgt     wfh    wfh_z    nl_2012_wfh_female_     43.5     2.1    0.326
    ----------------------------------------------------------------------------------


<!-- end of list -->

Troubleshooting data upload: JAMES executes checks on the conversion and ranges of the data. To gain efficiency, it does not automatically validate the input data against the specified JSON schema. JAMES writes diagnostic, sometimes cryptic, messages to the directory {session}/messages if it finds a problem. The user can rerun the data upload with two additional flags that request extra diagnostic output.

Example: Suppose we compromise the data by removing the required "clientDetails" and the optional "nestedDetails" sections. The mangled input data look like:

{"Format":"3.0","organisationCode":12345,"reference":"Maria's mangled data","clientMeasurements":[{"bdsNumber":235,"values":[{"date":"20181111","value":380},{"date":"20181211","value":435}]}]}

Everything appears normal if we read this data by the default:

fn <- "maria-mangled.json"
r5 <- james_post(host = host, path = "data/upload/json", txt = fn)
r5$parsed
$psn
  id                 name   src
1 -1 Maria's mangled data 12345

$xyz
  xname yname zname            zref  y
1   age   hgt hgt_z nl_1997_hgt__nl 38

If we upload with the additional validate = TRUE flag, JAMES runs the validation of the uploaded JSON against the JSON schema:

r6 <- james_post(host = host, path = "data/upload/json", txt = fn, validate = TRUE)
mess <- james_get(host = host, path = file.path(r6$session, "messages"))
cat(mess$parsed)

which indicates that the required JSON element "clientDetails" is missing. We inactivated the code chunk above because it may occasionally give the error “JAMES API request failed [400] child process has died In call: tryCatch()” when validate = TRUE. This is a known, not yet resolved issue.

We may drill down further by setting the intermediate = TRUE flag. This writes five JSON files that document the data flow into {session}/files/{*}.json.

For example, we can ask for the input data as read by JAMES by

r7 <- james_post(host = host, path = "data/upload/json", txt = fn, validate = TRUE, intermediate = TRUE)
url <- file.path(host, r7$session, "files/input.json")
url
[1] "https://james.groeidiagrammen.nl/x0605f6d4badca5/files/input.json"

With browseURL(url) we may view the file contents in the browser. The files directory contains five JSON files:

  1. files/input.json: the JSON input data;
  2. files/bds.json: a data frame with info per BDS number;
  3. files/ddi.json: result of recoding BDS into GSED item names;
  4. files/psn.json: known fixed child covariates;
  5. files/xy.json: time-varying variables.

Inspection of these files may uncover any problems with JAMES’s understanding of the data. If needed, study the underlying R source code at https://raw.githubusercontent.com/growthcharts/bdsreader/master/R/read_bds.R.

The validate and intermediate flag are useful for development and debugging. In production, we recommend leaving them at their default value (FALSE) and monitor any messages written to {session}/messages.

We start from child data in the file maria.json that we wish to process with JAMES. For testing purposes, you may change the values, but keep the general structure intact. The following curl commands uploads the file and processes the data.

curl -sF '[email protected]' -D headers $(cat .host)/data/upload/json > content
head content
{
  "psn": [
    {
      "id": -1,
      "name": "Maria",
      "dob": "2018-10-11",
      "dobm": "1990-12-02",
      "dobf": "1995-07-04",
      "src": "1234",
      "sex": "female",

Alternatively, we may read the file into a JSON string, and upload as follows:

JS=$(jq '.' maria.json | jq -sR '.')
curl -s $(cat .host)/data/upload/json -d "txt=$JS" > content

Finally, if the data are located at a URL, use

URL=$(cat .host)/ocpu/library/bdsreader/examples/maria.json
curl -s $(cat .host)/data/upload/json -d "txt='$URL'" > content

/charts/draw: Draw child data on growth chart

Maria is a preterm born at 27 weeks of gestational age. We already uploaded her data. We may now plot her growth data on the A4 chart for preterms as follows:

r5 <- james_post(host = host, 
                 path = "/charts/draw/svglite", 
                 session = r1$session,
                 chartcode = "PMAAN27", selector = "chartcode",
                 query = list(height = 29.7/2.54, width = 21/2.54))
writeLines(r5$parsed, con = filename_chart1)

Maria’s growth plotted on preterm chart, 0-15 months

Alternatively, we may upload data for a new child Laura and plot the data in one step:

fn <- system.file("extdata/bds_v3.0/smocc/Laura_S.json", package = "jamesdemodata")
r6 <- james_post(host = host,
                 path = "/charts/draw/svglite", txt = fn, 
                 chartcode = "NMBA", selector = "chartcode",
                 query = list(height = 29.7/2.54, width = 21/2.54))
writeLines(r6$parsed, con = filename_chart2)

For A4 sized charts, we recommend to generate the plot with query arguments list(height = 29.7/2.54, width = 21/2.54), as illustrated above. If you want to change the chart’s size in your HTML, use the out.width knitr chunk option, e.g. set out.width="500px". This gives the following output.

Laura’s growth plotted on chart for Dutch girls, 0-4 years

JAMES features a built-in prediction module based on curve matching. Suppose we want to predict Laura’s height at the 3y9m when Laura is 2 years old. The following chart plots 25 matches to Laura as grey curves. The variation between the grey curves at age 3y9m indicates the likely variation in the prediction. The blue line indicates Laura’s predicted height at age 3y9m.

r7 <- james_post(host = host, 
                 path = "/charts/draw/svglite", txt = fn, 
                 chartcode = "NMBH", dnr = "2-4",
                 lo = 2.0, hi = 3.75, nmatch = 25,
                 show_future = TRUE, show_realized = TRUE,
                 query = list(height = 18/2.54, width = 18/2.54))
writeLines(r7$parsed, con = filename_chart3)

For square charts, use query arguments list(height = 18/2.54, width = 18/2.54) to generate the plot. In order to get the same age units as the previous chart, calculate out.width as 500/21*18 = "429px".

Predict Laura’s future height at the age of 3y9m.

Upload maria.json and draw the height data on the default chart to produce an SVG file. Specify the proper width and height query parameters.

curl -sX 'POST' $(cat .host)'/charts/draw/svglite?width=7.09&height=7.09' \
-H 'accept: image/*' \
-F '[email protected];type=application/json' > maria1.svg

We need to set chartcode and selector parameters to choose a different chart.

curl -sX 'POST' $(cat .host)'/charts/draw/svglite?width=8.27&height=11.69' \
-H 'accept: image/*' \
-F "chartcode='PMAAN27'" \
-F "selector='chartcode'" \
-F '[email protected];type=application/json' > maria2.svg

An alternative is to read the data from a URL, and use the application/json protocol to specify parameters.

curl -sX 'POST' \
$(cat .host)'/charts/draw/svglite?width=8.27&height=11.69' \
-H 'accept: image/*' \
-H 'Content-Type: application/json' \
-d '{
"txt": "'$(cat .host)'/ocpu/library/jamesdemodata/extdata/bds_v3.0/smocc/Laura_S.json",
"chartcode" : "NMBA", 
"selector" : "chartcode"}' > laura.svg

/charts/list: List available growth charts

JAMES contains a wide variety of built-in growth charts. Each chart has a unique chartcode. A typical chart code looks like NJAA. We obtain the full list of chart codes as

r <- james_post(host = host, path = "charts/list/json")
charts <- r$parsed

The charts object is a data frame with 478 rows (charts) and the following variables:

names(charts)
[1] "chartgrp"   "chartcode"  "population" "sex"        "design"    
[6] "side"       "language"   "week"

JAMES contains charts for various child populations. There are charts for Down syndrome (DS), Hindustan (HS), Moroccan (MA), Dutch (NL)), preterm (PT) and Turkish (TU) children living in the Netherlands and the WHO Growth Standards (WHOblue, WHOpink). These charts contain references for height (hgt), weight (wgt), head circumference (hdc), weight-for-height (wfh), body mass index (bmi) and D-score (dsc), as well as combined charts with multiple references on A4 format (front, back, -hdc).

The most important index variables are population and side:

with(charts, table(population, side))
          side
population -hdc back bmi dsc front hdc hgt wfh wgt
   DS         0    6   2   0     6   6   6   4   2
   HS         4    2   2   0     6   2   6   4   2
   MA         0    6   2   0     6   6   6   4   2
   NL         2    8   2   4     8   8   8   4   4
   PT         0   24   0  48    48  24  48   0  48
   TU         0    6   2   0     6   6   6   4   2
   WHOblue    0    0   0  26     2   1   2   1   1
   WHOpink    0    0   0  26     2   1   2   1   1

The URL {host}/site (see below) displays the currently active chart code as a field in the left sidebar.

Restrict the listing to the WHO references:

curl -sX 'POST' \
$(cat .host)'/charts/list/json' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"chartgrp": "who"
}'

/charts/validate: Validate chart codes

The /charts/validate end point attempt to find one or more user-specified chart codes. For example, the following cocde checkc five chart codes:

r <- james_post(host = host, 
                path = "charts/validate/json", 
                chartcode = c("NMAW", "NJAb", "PJAAN23", "PJAAN25", "dummy"))
r$parsed
[1]  TRUE FALSE FALSE  TRUE FALSE

Check five chart codes:

curl -sX 'POST' \
$(cat .host)'/charts/validate/json' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"chartcode": [
"NMAW",
"NJAB",
"PJAAN23",
"PJAAN25", 
"dummy"
]
}'
[true, true, false, true, false]

/dscore/calculate: Calculate D-score

The D-score is a developmental score that is used to assess the development of children. The /dscore/calculate end point calculates the D-score for a child based on the uploaded data. The D-score is a continuous measure of child development, and it is calculated based on the child’s age and developmental milestones.

fn <- system.file("extdata/bds_v3.0/smocc/Laura_S.json", package = "jamesdemodata")
r <- james_post(host = host, path = "dscore/calculate/json", output = "table", txt = fn)
r
JAMES request:
  Path    : dscore/calculate/json
  Status  : 201
  Session : x0529e93d10e5da

Parsed response:
'data.frame':   9 obs. of  9 variables:
 $ age  : num  0.101 0.159 0.235 0.485 0.753 ...
 $ xname: chr  "age" "age" "age" "age" ...
 $ yname: chr  "dsc" "dsc" "dsc" "dsc" ...
 $ zname: chr  "dsc_z" "dsc_z" "dsc_z" "dsc_z" ...
 $ zref : chr  "ph_2023_dsc_female_40" "ph_2023_dsc_female_40" "ph_2023_dsc_female_40" "ph_2023_dsc_female_40" ...
 $ x    : num  0.101 0.159 0.235 0.485 0.753 ...
 $ y    : num  15.7 18 20.9 25.9 44.6 ...
 $ z    : num  -0.058 0.022 -0.018 -1.476 0.908 ...
 $ date : chr  "19890227" "19890320" "19890417" "19890717" ...
head(r$parsed)
     age xname yname zname                  zref      x     y      z     date
1 0.1013   age   dsc dsc_z ph_2023_dsc_female_40 0.1013 15.68 -0.058 19890227
2 0.1588   age   dsc dsc_z ph_2023_dsc_female_40 0.1588 18.01  0.022 19890320
3 0.2355   age   dsc dsc_z ph_2023_dsc_female_40 0.2355 20.93 -0.018 19890417
4 0.4846   age   dsc dsc_z ph_2023_dsc_female_40 0.4846 25.89 -1.476 19890717
5 0.7529   age   dsc dsc_z ph_2023_dsc_female_40 0.7529 44.58  0.908 19891023
6 1.0212   age   dsc dsc_z ph_2023_dsc_female_40 1.0212 51.59  0.900 19900129

The D-score is found in column y and the DAZ is in column z.

/vwc/select: Select Van Wiechen development milestones based on D-score and age

The Van Wiechenonderzoek is a Dutch screening tool used to track a child’s development from birth to age 4.5. It includes 75 milestones across three areas: fine motor skills, gross motor skills, and communication. The /vwc/select end point suggest the most fitting milestones for a child based on the uploaded data.

fn <- system.file("extdata/bds_v3.0/smocc/Laura_S.json", package = "jamesdemodata")
r <- james_post(host = host, path = "vwc/select/json", output = "table", txt = fn, percentiles = TRUE)
r
JAMES request:
  Path    : vwc/select/json
  Status  : 201
  Session : x035849da6cacbd

Parsed response:
[1] "ddifmd027\nddigmm073\nddifmd023\nddicmm050\nddicmm047\nddigmd074"
head(r$parsed)
[1] "ddifmd027\nddigmm073\nddifmd023\nddicmm050\nddicmm047\nddigmd074"

To be added

/vwc/percentiles: Select Van Wiechen development milestones with detailed cut-off information

It is also possible to obtain a table of age and D-score percentiles for a selection of milestones. This can be done either directly using the vwc argument, or indirectly from uploaded child data.

r <- james_post(host = host, path = "vwc/percentiles/json", output = "table", vwc = "ddifmd018") 
head(r$parsed)
       item      D2     D10  D50     D90     D98      A2     A10     A50
1 ddifmd018 52.1101 55.6762 60.3 64.9238 68.4899 13.9437 16.4593 20.4204
      A90     A98
1 25.3019 29.7694

To be added

/dcat/calculate: Calculate next milestone item

The DCAT allows users to performa adapative testing using either ddi or gs1 items.

The following snippet uploads child data and requests the next item to be tested based on the gs1 instrument, the default.

fn <- system.file("examples", "example_v3.1.json", package = "bdsreader")
r <- james_post(host = host, path = "dcat/calculate/json", txt = fn)
r
JAMES request:
  Path    : dcat/calculate/json
  Status  : 201
  Session : x070cb6c9e5876f

Parsed response:
[1] "gs1lgc110"
head(r$parsed)
[1] "gs1lgc110"

By specifying the instrument we can specify whether we want to include ddi or gs1 (or even both). In the case of only ddi items, we must additionally specify a key that supports the ddi instrument.

fn <- system.file("examples", "example_v3.1.json", package = "bdsreader")
r <- james_post(host = host, path = "dcat/calculate/json", txt = fn, instrument = "ddi", key = "gsed2406")
r
JAMES request:
  Path    : dcat/calculate/json
  Status  : 201
  Session : x088155906b2cc1

Parsed response:
[1] "ddifmd020"
head(r$parsed)
[1] "ddifmd020"

To be added

/screeners/list: List available growth screeners

JAMES implements several screening algorithms. The /screeners/list end point provides detailed information on each of these.

r <- james_post(host = host, path = "/screeners/list/json", 
                session = r1$session)
names(r$parsed)
[1] "Versie"                "yname"                 "Categorie"            
[4] "CategorieOmschrijving" "JGZRichtlijn"          "Code"                 
[7] "CodeOmschrijving"     
with(r$parsed, table(yname, Categorie))
     Categorie
yname 1000 2000 3000
  hdc    0    0   17
  hgt   45    0    0
  wgt    0   26    0

There are currently 88 different codes. Codes ending in 31, e.g., 1031 or 2031 indicate normal growth, whereas code ending in 41, 42 and so on, signal that - according to the guidelines - the child should be referred for further investigation.

We get the details for the guidelines for head circumference as

curl -sX 'POST' \
$(cat .host)'/screeners/list/json' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{"ynames": "hdc"}'

/screeners/apply: Apply growth screeners to child data

The /screeners/apply end point applies standard screeners to the child data. Invoke the screeners by

r <- james_post(host = host, path = "/screeners/apply/json", 
                session = r1$session)
r$parsed
  Categorie CategorieOmschrijving Code
1      1000                Lengte 1031
2      2000               Gewicht 2031
3      3000           Hoofdomtrek 3031
                                                                                                               CodeOmschrijving
1 Het advies volgens de JGZ-richtlijn lengtegroei is als volgt: In principe geen verwijzing nodig, naar eigen inzicht handelen.
2 Het advies volgens de JGZ-richtlijn overgewicht is als volgt: In principe geen verwijzing nodig, naar eigen inzicht handelen.
3                                                               In principe geen verwijzing nodig, naar eigen inzicht handelen.
  Versie Leeftijd
1 1.24.0    0.167
2 1.24.0    0.167
3 1.24.0    0.167

The procedure

  1. calculates, per outcome, the intervals between the most recent measurement and all earlier measurements;
  2. tests whether any of those intervals produces a signal according the screening algorithm;
  3. reports the most recent non-standard signal that indicate abnormal growth.

In the example, all returned codes (1031, 2031, 3031) end in “31”, which signals normal growth. The full table of return codes and messages can be obtained by the /screeners/list end point (see above).

There are several possibilities to visualise and integrate multiple evaluations per curve performed in step 2 into one advice. Before May 2023, JAMES returned an advice for each combination of time point and outcome, but that table presented a lot of output that was difficult to act one. Since May 2023, JAMES reports only one signal per curve.

curl -sX 'POST' \
$(cat .host)'/screeners/apply/json' \
-H 'accept: application/json' \
-H 'Content-Type: multipart/form-data' \
-F '[email protected];type=application/json'
[
  {
    "Categorie": 1000,
    "CategorieOmschrijving": "Lengte",
    "Code": 1031,
    "CodeOmschrijving": "Het advies volgens de JGZ-richtlijn lengtegroei is als volgt: In principe geen verwijzing nodig, naar eigen inzicht handelen.",
    "Versie": "1.24.0",
    "Leeftijd": 0.167
  },
  {
    "Categorie": 2000,
    "CategorieOmschrijving": "Gewicht",
    "Code": 2031,
    "CodeOmschrijving": "Het advies volgens de JGZ-richtlijn overgewicht is als volgt: In principe geen verwijzing nodig, naar eigen inzicht handelen.",
    "Versie": "1.24.0",
    "Leeftijd": 0.167
  },
  {
    "Categorie": 3000,
    "CategorieOmschrijving": "Hoofdomtrek",
    "Code": 3031,
    "CodeOmschrijving": "In principe geen verwijzing nodig, naar eigen inzicht handelen.",
    "Versie": "1.24.0",
    "Leeftijd": 0.167
  }
]

/site: Request an empty site

The /site end point provides interactive site containing all charts, but without child data. This end point is primarily useful to obtain a quick overview of the available charts.

browseURL(file.path(host, "site"))

/site/request: Request personalised site

The /site/request end point creates an URL to a personalised, interactive site containing all charts.

r <- james_post(host = host, path = "/site/request/json", 
                sitehost = host, txt = js)
r$parsed
[1] "https://james.groeidiagrammen.nl/site?session=x001f7fae6fb71b"

Run the command and paste the generated URL in the address field of your browser. The starting chart is chosen by JAMES and depends on the age of the child.

The above command fails for localhost:8080, and gives the error [400] Couldn't connect to server [localhost], because localhost:8080 does not allow nested requests to itself.

The solution is the break the request into two steps. First, we create a session by uploading the child data, and then we use that session to create the URL to the personalised site. We start by uploading data to JAMES and het r$session. After that, we can use either site\request or manual construction of the URL to the site:

fn <- system.file("examples/maria.json", package = "bdsreader")
r <- james_post(host = host, path = "data/upload/json", txt = fn)

# URL construction by /site/request
site1 <- james_post(host = host, path = "site/request/json", 
                sitehost = host, session = r$session, upload = FALSE)
site1
JAMES request:
  Path    : site/request/json
  Status  : 201
  Session : x0d51ab98297be1

Parsed response:
[1] "https://james.groeidiagrammen.nl/site?session=x0f8088d76905b8"
# Or manual URL construction (faster) 
site2 <- modify_url(url = host, path = "site", query = list(session = r$session))
site2
[1] "https://james.groeidiagrammen.nl/site?session=x0f8088d76905b8"
# browseURL(site1)

Paste the generated URL in the address field of your browser. The initial page shown depends on the child’s age. This two-step approach also works for remote servers. In practice, use the two-step approach is stabler and more reliable.

curl -sX 'POST' \
$(cat .host)'/site/request/json' \
-H 'accept: application/json' \
-H 'Content-Type: multipart/form-data' \
-F "sitehost='$(cat .host)'" \
-F '[email protected];type=application/json'
["https://james.groeidiagrammen.nl/site?session=x0e55188fafcf47"]

/blend/request: Obtain a blend from multiple end points

The /blend/request end point returns the results of multiple end points, and thus functions as a one-stop shop. However, currently it does not support graphics output, so use /{session}/{info}/svglite or /charts/draw/svglite for the charts.

As before, the following command only works on remote servers, not on localhost:8080.

fn <- system.file("extdata", "bds_v3.0", "smocc", "Laura_S.json",
                 package = "jamesdemodata", mustWork = TRUE)
r <- james_post(host = host, path = "/blend/request/json", 
                sitehost = host, txt = fn)
r
JAMES request:
  Path    : /blend/request/json
  Status  : 201
  Session : x0fdf730c2b937a

Parsed response:
List of 6
 $ txt      : chr "{\"Format\": \"3.0\",\"organisationCode\": 0,\"reference\": \"Laura S\",\"clientDetails\": [{\"bdsNumber\": 19,"| __truncated__
 $ session  : chr "x0f2d7e10bc3ba2"
 $ site     : chr "https://james.groeidiagrammen.nl/site?session=x0f2d7e10bc3ba2"
 $ child    :'data.frame':  1 obs. of  12 variables:
  ..$ id  : int -1
  ..$ name: chr "Laura S"
  ..$ dob : chr "1989-01-21"
  ..$ dobm: chr "1961-07-22"
  ..$ src : chr "0"
  ..$ sex : chr "female"
  ..$ gad : int 276
  ..$ ga  : int 39
  ..$ smo : int 0
  ..$ bw  : int 2950
  ..$ hgtm: int 164
  ..$ hgtf: int 179
 $ time     :'data.frame':  58 obs. of  8 variables:
  ..$ age  : num [1:58] 0 0.101 0.159 0.235 0.485 ...
  ..$ xname: chr [1:58] "age" "age" "age" "age" ...
  ..$ yname: chr [1:58] "hgt" "hgt" "hgt" "hgt" ...
  ..$ zname: chr [1:58] "hgt_z" "hgt_z" "hgt_z" "hgt_z" ...
  ..$ zref : chr [1:58] "nl_1997_hgt_female_nl" "nl_1997_hgt_female_nl" "nl_1997_hgt_female_nl" "nl_1997_hgt_female_nl" ...
  ..$ x    : num [1:58] 0 0.101 0.159 0.235 0.485 ...
  ..$ y    : num [1:58] 48 53.5 56 59.5 65.5 71.5 75 80 84 90 ...
  ..$ z    : num [1:58] -1.515 -0.499 -0.261 0.163 -0.259 ...
 $ screeners:'data.frame':  3 obs. of  6 variables:
  ..$ Categorie            : int [1:3] 1000 2000 3000
  ..$ CategorieOmschrijving: chr [1:3] "Lengte" "Gewicht" "Hoofdomtrek"
  ..$ Code                 : int [1:3] 1031 2075 3021
  ..$ CodeOmschrijving     : chr [1:3] "Het advies volgens de JGZ-richtlijn lengtegroei is als volgt: In principe geen verwijzing nodig, naar eigen inzicht handelen." "Het advies volgens de JGZ-richtlijn ondergewicht is als volgt: Sterke gewichtsafname (-1 SD), advies: Is er spr"| __truncated__ "De richtlijn hoofdomtrek is bedoeld voor kinderen tot 1 jaar."
  ..$ Versie               : chr [1:3] "1.24.0" "1.24.0" "1.24.0"
  ..$ Leeftijd             : num [1:3] 2.04 2.04 2.04
names(r$parsed)
[1] "txt"       "session"   "site"      "child"     "time"      "screeners"

Getting this to work on localhost:8080, use two steps:

resp1 <- james_post(host = host, path = "data/upload/json", txt = fn)
resp2 <- james_post(host = host, path = "blend/request/json", session = resp1$session)
names(resp2$parsed)
[1] "txt"       "session"   "site"      "child"     "time"      "screeners"
# browseURL(resp2$parsed$site)
curl -sX 'POST' \
$(cat .host)'/blend/request/json' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"txt": "'$(cat .host)'/ocpu/library/bdsreader/examples/Laura_S.json",
"sitehost": "'$(cat .host)'",
"blend": "standard"
}'
{
  "txt": "https://james.groeidiagrammen.nl/ocpu/library/bdsreader/examples/Laura_S.json",
  "session": "x09ca023e698432",
  "site": "https://james.groeidiagrammen.nl/site?session=x09ca023e698432",
  "child": [
    {
      "id": -1,
      "name": "Laura S",
      "dob": "1989-01-21",
      "dobm": "1961-07-22",
      "src": "0",
      "sex": "female",
      "gad": 276,
      "ga": 39,
      "smo": 0,
      "bw": 2950,
      "hgtm": 164,
      "hgtf": 179
    }
  ],
  "time": [
    {
      "age": 0,
      "xname": "age",
      "yname": "hgt",
      "zname": "hgt_z",
      "zref": "nl_1997_hgt_female_nl",
      "x": 0,
      "y": 48,
      "z": -1.515
    },
    {
      "age": 0.1013,
      "xname": "age",
      "yname": "hgt",
      "zname": "hgt_z",
      "zref": "nl_1997_hgt_female_nl",
      "x": 0.1013,
      "y": 53.5,
      "z": -0.499
    },
    {
      "age": 0.1588,
      "xname": "age",
      "yname": "hgt",
      "zname": "hgt_z",
      "zref": "nl_1997_hgt_female_nl",
      "x": 0.1588,
      "y": 56,
      "z": -0.261
    },
    {
      "age": 0.2355,
      "xname": "age",
      "yname": "hgt",
      "zname": "hgt_z",
      "zref": "nl_1997_hgt_female_nl",
      "x": 0.2355,
      "y": 59.5,
      "z": 0.163
    },
    {
      "age": 0.4846,
      "xname": "age",
      "yname": "hgt",
      "zname": "hgt_z",
      "zref": "nl_1997_hgt_female_nl",
      "x": 0.4846,
      "y": 65.5,
      "z": -0.259
    },
    {
      "age": 0.7529,
      "xname": "age",
      "yname": "hgt",
      "zname": "hgt_z",
      "zref": "nl_1997_hgt_female_nl",
      "x": 0.7529,
      "y": 71.5,
      "z": 0.131
    },
    {
      "age": 1.0212,
      "xname": "age",
      "yname": "hgt",
      "zname": "hgt_z",
      "zref": "nl_1997_hgt_female_nl",
      "x": 1.0212,
      "y": 75,
      "z": -0.18
    },
    {
      "age": 1.2512,
      "xname": "age",
      "yname": "hgt",
      "zname": "hgt_z",
      "zref": "nl_1997_hgt_female_nl",
      "x": 1.2512,
      "y": 80,
      "z": 0.421
    },
    {
      "age": 1.5387,
      "xname": "age",
      "yname": "hgt",
      "zname": "hgt_z",
      "zref": "nl_1997_hgt_female_nl",
      "x": 1.5387,
      "y": 84,
      "z": 0.527
    },
    {
      "age": 2.0397,
      "xname": "age",
      "yname": "hgt",
      "zname": "hgt_z",
      "zref": "nl_1997_hgt_female_nl",
      "x": 2.0397,
      "y": 90,
      "z": 0.67
    },
    {
      "age": 0,
      "xname": "age",
      "yname": "wgt",
      "zname": "wgt_z",
      "zref": "nl_1997_wgt_female_nl",
      "x": 0,
      "y": 2.95,
      "z": -1.055
    },
    {
      "age": 0.1013,
      "xname": "age",
      "yname": "wgt",
      "zname": "wgt_z",
      "zref": "nl_1997_wgt_female_nl",
      "x": 0.1013,
      "y": 4.18,
      "z": -0.162
    },
    {
      "age": 0.1588,
      "xname": "age",
      "yname": "wgt",
      "zname": "wgt_z",
      "zref": "nl_1997_wgt_female_nl",
      "x": 0.1588,
      "y": 5,
      "z": 0.401
    },
    {
      "age": 0.2355,
      "xname": "age",
      "yname": "wgt",
      "zname": "wgt_z",
      "zref": "nl_1997_wgt_female_nl",
      "x": 0.2355,
      "y": 5.9,
      "z": 0.717
    },
    {
      "age": 0.4846,
      "xname": "age",
      "yname": "wgt",
      "zname": "wgt_z",
      "zref": "nl_1997_wgt_female_nl",
      "x": 0.4846,
      "y": 8.24,
      "z": 1.173
    },
    {
      "age": 0.7529,
      "xname": "age",
      "yname": "wgt",
      "zname": "wgt_z",
      "zref": "nl_1997_wgt_female_nl",
      "x": 0.7529,
      "y": 9.65,
      "z": 1.052
    },
    {
      "age": 1.0212,
      "xname": "age",
      "yname": "wgt",
      "zname": "wgt_z",
      "zref": "nl_1997_wgt_female_nl",
      "x": 1.0212,
      "y": 10.95,
      "z": 1.164
    },
    {
      "age": 1.2512,
      "xname": "age",
      "yname": "wgt",
      "zname": "wgt_z",
      "zref": "nl_1997_wgt_female_nl",
      "x": 1.2512,
      "y": 11.9,
      "z": 1.247
    },
    {
      "age": 1.5387,
      "xname": "age",
      "yname": "wgt",
      "zname": "wgt_z",
      "zref": "nl_1997_wgt_female_nl",
      "x": 1.5387,
      "y": 12.8,
      "z": 1.228
    },
    {
      "age": 2.0397,
      "xname": "age",
      "yname": "wgt",
      "zname": "wgt_z",
      "zref": "nl_1997_wgt_female_nl",
      "x": 2.0397,
      "y": 13.9,
      "z": 0.989
    },
    {
      "age": 0.1013,
      "xname": "age",
      "yname": "hdc",
      "zname": "hdc_z",
      "zref": "nl_1997_hdc_female_nl",
      "x": 0.1013,
      "y": 37.6,
      "z": 0.418
    },
    {
      "age": 0.1588,
      "xname": "age",
      "yname": "hdc",
      "zname": "hdc_z",
      "zref": "nl_1997_hdc_female_nl",
      "x": 0.1588,
      "y": 39,
      "z": 0.605
    },
    {
      "age": 0.2355,
      "xname": "age",
      "yname": "hdc",
      "zname": "hdc_z",
      "zref": "nl_1997_hdc_female_nl",
      "x": 0.2355,
      "y": 40.5,
      "z": 0.696
    },
    {
      "age": 0.4846,
      "xname": "age",
      "yname": "hdc",
      "zname": "hdc_z",
      "zref": "nl_1997_hdc_female_nl",
      "x": 0.4846,
      "y": 44.1,
      "z": 1.021
    },
    {
      "age": 0.7529,
      "xname": "age",
      "yname": "hdc",
      "zname": "hdc_z",
      "zref": "nl_1997_hdc_female_nl",
      "x": 0.7529,
      "y": 46.6,
      "z": 1.418
    },
    {
      "age": 1.0212,
      "xname": "age",
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  ],
  "screeners": [
    {
      "Categorie": 1000,
      "CategorieOmschrijving": "Lengte",
      "Code": 1031,
      "CodeOmschrijving": "Het advies volgens de JGZ-richtlijn lengtegroei is als volgt: In principe geen verwijzing nodig, naar eigen inzicht handelen.",
      "Versie": "1.24.0",
      "Leeftijd": 2.0397
    },
    {
      "Categorie": 2000,
      "CategorieOmschrijving": "Gewicht",
      "Code": 2075,
      "CodeOmschrijving": "Het advies volgens de JGZ-richtlijn ondergewicht is als volgt: Sterke gewichtsafname (-1 SD), advies: Is er sprake van een afwijkende voedingstoestand en/of klachten of symptomen die kunnen wijzen op onderliggende ziekte of problemen? Indien ja, Verwijzen naar kinderarts. Indien nee, dan is er in principe geen verwijzing nodig. Naar eigen inzicht handelen.",
      "Versie": "1.24.0",
      "Leeftijd": 2.0397
    },
    {
      "Categorie": 3000,
      "CategorieOmschrijving": "Hoofdomtrek",
      "Code": 3021,
      "CodeOmschrijving": "De richtlijn hoofdomtrek is bedoeld voor kinderen tot 1 jaar.",
      "Versie": "1.24.0",
      "Leeftijd": 2.0397
    }
  ]
}

Resources

Internal

Description Status
Changelog open
OpenAPI specification open
JSON data schema 3.0 open
Source files open
JAMES issue tracker open

External

Description Status
JAMES demo current
Basisdataset JGZ current
OpenCPU API current