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Job request: 2034

Organisation:
Bennett Institute
Workspace:
surgery-outcomes-full
ID:
tmzu65afkxmtwllo
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 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.

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: 04:47:01

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

Job request

Status
Succeeded
Backend
TPP
Workspace
surgery-outcomes-full
Requested by
Helen Curtis
Branch
master
Force run dependencies
No
Git commit hash
04177ab
Requested actions
  • generate_study_population
  • generate_study_population_2019
  • filter_population_for_matching
  • matching
  • generate_notebook_data_checks

Code comparison

Compare the code used in this job request