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Job request: 3027

Organisation:
Workspace:
sro-measure-cam-test
ID:
kvmqpcbrhu4co6og

This page shows the technical details of what happened when the authorised researcher Chris Yeung requested one or more actions to be run against real patient data within a secure environment.

By cross-referencing the list of jobs with the pipeline section below, you can infer what security level the outputs were written to.

The output security levels are:

  • highly_sensitive
    • Researchers can never directly view these outputs
    • Researchers can only request code is run against them
  • moderately_sensitive
    • Can be viewed by an approved researcher by logging into a highly secure environment
    • These are the only outputs that can be requested for public release via a controlled output review service.

Pipeline

Show project.yaml
version: '3.0'

expectations:
  population_size: 1000
   
    
actions:

  generate_study_population:
    run: cohortextractor:latest generate_cohort --study-definition study_definition --index-date-range "2019-01-01 to 2021-02-01 by month" --output-dir=output
    outputs:
      highly_sensitive:
        cohort: output/input_*.csv

  generate_study_population_practice_count:
    run: cohortextractor:latest generate_cohort --study-definition study_definition_practice_count --output-dir=output
    outputs:
      highly_sensitive:
        cohort: output/input_practice_count.csv

  generate_measures:
      run: cohortextractor:latest generate_measures --study-definition study_definition --output-dir=output
      needs: [generate_study_population]
      outputs:
        moderately_sensitive:
          measure_csv: output/measure_*.csv
  
  get_practice_count:
    run: python:latest python analysis/get_practice_count.py
    needs: [generate_study_population_practice_count]
    outputs:
      moderately_sensitive:
        text: output/practice_count.json
  
  get_patient_count:
    run: python:latest python analysis/get_patients_counts.py
    needs: [generate_study_population]
    outputs:
      moderately_sensitive:
        text: output/patient_count.json
        


        
  
  generate_notebook:
    run: jupyter:latest jupyter nbconvert /workspace/notebooks/sentinel_measures.ipynb --execute --to html --output-dir=/workspace/output --ExecutePreprocessor.timeout=86400 --no-input
    needs: [generate_measures, get_practice_count, get_patient_count]
    outputs:
      moderately_sensitive:
        notebook: output/sentinel_measures.html
     
  
  # generate_notebook_practice:
  #   run: jupyter:latest jupyter nbconvert /workspace/notebooks/sentinel_measures_by_practice.ipynb --execute --to html --output-dir=/workspace/output --ExecutePreprocessor.timeout=86400 --no-input
  #   needs: [generate_measures, get_practice_count, get_patient_count]
  #   outputs:
  #     moderately_sensitive:
  #       notebook: output/sentinel_measures_by_practice.html
  #       csvs: output/*_check.csv

  get_event_summary:
    run: python:latest python analysis/get_event_summary.py
    needs: [generate_study_population]
    outputs:
      moderately_sensitive:
        text: output/*_event_summary.csv

Timeline

  • Created:

  • Started:

  • Finished:

  • Runtime:

These timestamps are generated and stored using the UTC timezone on the GRAPHNET backend.

Job request

Status
Failed
JobRequestError: Internal error
Backend
Graphnet
Requested by
Chris Yeung
Branch
graphnet
Force run dependencies
No
Git commit hash
049f0cf
Requested actions
  • generate_study_population_practice_count
  • generate_measures

Code comparison

Compare the code used in this job request