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Job request: 26839

Organisation:
University of Oxford
Workspace:
vitamin-d-trends
ID:
fmkbmubnvjhc5exu
View repo View project.yaml

This page shows the technical details of what happened when the authorised researcher Jaidip Gill 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

  • Action:
    generate_measures
    Status:
    Running
    Job identifier:
    oqf7qdnfxbc3bjz4
  • Action:
    generate_covariate_plots
    Status:
    Pending
    Job identifier:
    cutgo2cmvf4hwhpf
    Status message:
    waiting_on_dependencies: Waiting on dependencies
  • Action:
    generate_decile_plots
    Status:
    Pending
    Job identifier:
    fxxsr34mgethtyrd
    Status message:
    waiting_on_dependencies: Waiting on dependencies
  • Action:
    generate_overall_trends
    Status:
    Pending
    Job identifier:
    6hipw6vgwmxnnvhd
    Status message:
    waiting_on_dependencies: Waiting on dependencies

Pipeline

Show project.yaml 
version: '5.0'

actions:

  generate_measures:
    run: ehrql:v1 generate-measures analysis/n50_measures.py
      --output output/measures.csv.gz
      --
      --full
    outputs:
      highly_sensitive:
        measures: output/measures.csv.gz

  generate_measures_test:
    run: ehrql:v1 generate-measures analysis/n50_measures.py
      --output output/measures_test.csv
      --
      --test
    outputs:
      highly_sensitive:
        measures: output/measures_test.csv


  assure_dataset:
    run: >
        ehrql:v1 generate-dataset
        analysis/n60_assure_definition.py
        --test-data-file analysis/n70_test_dataset.py
        --output output/dataset.csv
        --
        --assure
    outputs:
      highly_sensitive:
        population: output/dataset.csv


  generate_overall_trends:
    run: >
      r:v2 analysis/n91_overall_trends.R --full
    needs:
      - generate_measures
    outputs:
      moderately_sensitive:
        vitd_overall_trends: output/vitd_overall_trends.png

  generate_overall_trends_test:
    run: >
      r:v2 analysis/n91_overall_trends.R --test
    needs:
      - generate_measures_test
    outputs:
      moderately_sensitive:
        vitd_overall_trends_test: output/vitd_overall_trends_test.png
   

  generate_decile_plots:
    run: >
      r:v2 analysis/n92_practice_decile_plots.R --full
    needs:
      - generate_measures
    outputs:
      moderately_sensitive:
         vitd_practice_deciles: output/vitd_practice_deciles.png

  generate_decile_plots_test:
    run: >
      r:v2 analysis/n92_practice_decile_plots.R --test
    needs:
      - generate_measures_test
    outputs:
      moderately_sensitive:
        vitd_practice_deciles_test: output/vitd_practice_deciles_test.png


  generate_covariate_plots:
    run: >
      r:v2 analysis/n93_covariate_plots.R --full
    needs:
      - generate_measures
    outputs:
      moderately_sensitive:
        vitd_covariates_demographic_testing: output/vitd_covariates_demographic_testing.png
        vitd_covariates_demographic_prescribing: output/vitd_covariates_demographic_prescribing.png
        vitd_covariates_demographic_vit_d_calcium_prescribing: output/vitd_covariates_demographic_vit_d_calcium_prescribing.png
        vitd_covariates_geographic_practice_testing: output/vitd_covariates_geographic_practice_testing.png
        vitd_covariates_geographic_practice_prescribing: output/vitd_covariates_geographic_practice_prescribing.png
        vitd_covariates_geographic_practice_vit_d_calcium_prescribing: output/vitd_covariates_geographic_practice_vit_d_calcium_prescribing.png

  generate_covariate_plots_test:
    run: >
      r:v2 analysis/n93_covariate_plots.R --test
    needs:
      - generate_measures_test
    outputs:
      moderately_sensitive:
        vitd_covariates_demographic_testing_test: output/vitd_covariates_demographic_testing_test.png
        vitd_covariates_demographic_prescribing_test: output/vitd_covariates_demographic_prescribing_test.png
        vitd_covariates_demographic_vit_d_calcium_prescribing_test: output/vitd_covariates_demographic_vit_d_calcium_prescribing_test.png
        vitd_covariates_geographic_practice_testing_test: output/vitd_covariates_geographic_practice_testing_test.png
        vitd_covariates_geographic_practice_prescribing_test: output/vitd_covariates_geographic_practice_prescribing_test.png
        vitd_covariates_geographic_practice_vit_d_calcium_prescribing_test: output/vitd_covariates_geographic_practice_vit_d_calcium_prescribing_test.png

Job statistics

Status Count Percentage
Pending 3 75%
Running 1 25%
Succeeded 0 0%
Failed 0 0%

0 / 4 (0%) complete

Timeline

  • Created:

  • Started:

  • Runtime:

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

Job request

Status
Running
Backend
TPP
Workspace
vitamin-d-trends
Requested by
Jaidip Gill
Branch
main
Force run dependencies
No
Git commit hash
e31b819
Requested actions
  • generate_measures
  • generate_overall_trends
  • generate_decile_plots
  • generate_covariate_plots

Code comparison

Compare the code used in this job request