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Job request: 19599

Organisation:
University of Manchester
Workspace:
adverse-event-prediction-model
ID:
gc7netf4zl6hmfur
View repo View project.yaml

This page shows the technical details of what happened when the authorised researcher Billy Zhong 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: 30000

actions:
### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ###
### study cohort ###

  generate_study_population_period_1:
    run: cohortextractor:latest generate_cohort --study-definition study_definition_period_1
    outputs:
      highly_sensitive:
        cohort: output/input_period_1.csv

  period_1_process:
    run: r:latest analysis/period_1_process.R
    needs: [generate_study_population_period_1]
    outputs:
      highly_sensitive: 
        csv1: output/period_1a.csv

Timeline

  • Created:

  • Started:

  • Finished:

  • Runtime: 00:40:20

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

Job information

Status
Succeeded
Backend
TPP
Workspace
adverse-event-prediction-model
Requested by
Billy Zhong
Branch
ae_prediction
Force run dependencies
No
Git commit hash
51da4a6
Requested actions
  • generate_study_population_period_1

Code comparison

Compare the code used in this Job Request