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Job request: 1538

Organisation:
Bennett Institute
Workspace:
surgery-outcomes-full
ID:
j3ox3u6siyvhwnry
View repo View project.yaml

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

By cross-referencing the list of jobs with the pipeline section below, you can infer what security level various outputs were written to. Researchers can never directly view outputs marked as highly_sensitive ; they can only request that code runs against them. Outputs marked as moderately_sensitive can be viewed by an approved researcher by logging into a highly secure environment. Only outputs marked as moderately_sensitive can be requested for release to the public, via a controlled output review service.

Jobs

Pipeline

Show project.yaml 
version: '3.0'

expectations:
  population_size: 5000

actions:

  generate_study_population:
    run: cohortextractor:latest generate_cohort --study-definition study_definition --index-date-range "2020-04-01 to 2020-04-01 by month"
    outputs:
      highly_sensitive:
        cohort: output/input_2020*.csv


  generate_study_population_2019:
    run: cohortextractor:latest generate_cohort --study-definition study_definition --index-date-range "2019-04-01 to 2019-04-01 by month"
    outputs:
      highly_sensitive:
        cohort: output/input_2019*.csv

  filter_population_for_matching:
    run: python:latest python analysis/filter_population.py
    needs: [generate_study_population]
    outputs:
      highly_sensitive:
        filtered_cohort: output/filtered_2020_for_matching.csv
        covid_cohort: output/filtered_2020_covid_positive.csv
        
  matching:
    run: python:latest python analysis/match_running.py
    needs: [generate_study_population, filter_population_for_matching, generate_study_population_2019]
    outputs:
      moderately_sensitive:
        matching_report: output/matching_report_2019.txt
      highly_sensitive:
        matched_cohort: output/matched_matches_2019.csv

  generate_notebook_data_checks:
    run: jupyter:latest jupyter nbconvert /workspace/notebooks/data_checks.ipynb --execute --to html --output-dir=/workspace/output --ExecutePreprocessor.timeout=86400
    needs: [filter_population_for_matching]
    outputs:
      moderately_sensitive:
        notebook: output/data_checks.html

  generate_notebook:
    run: jupyter:latest jupyter nbconvert /workspace/notebooks/analysis.ipynb --execute --to html --output-dir=/workspace/output --ExecutePreprocessor.timeout=86400
    needs: [generate_study_population_2019, filter_population_for_matching, matching]
    outputs:
      moderately_sensitive:
        notebook: output/analysis.html
        baseline_char: output/baseline*
        counts: output/patient_count*
        outcomes_summary: output/summary*
        outcomes_detailed: output/detailed*
        outcomes_mortality: output/mortality*

Timeline

  • Created:

  • Started:

  • Finished:

  • Runtime: 00:00:36

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

Job information

Status
Succeeded
Backend
TPP
Workspace
surgery-outcomes-full
Requested by
Helen Curtis
Branch
master
Force run dependencies
No
Git commit hash
02c6ccf
Requested actions
  • generate_notebook

Code comparison

Compare the code used in this Job Request