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Job request: 5174

Organisation:
University of Manchester
Workspace:
service_eval_work
ID:
oofaa7jmpuduobgr
View repo View project.yaml

This page shows the technical details of what happened when the authorised researcher Vicki Palin UoM 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: 1000

actions:

  generate_study_population:
    run: cohortextractor:latest generate_cohort --study-definition study_definition --index-date-range "2019-01-01 to today by month" --skip-existing --output-dir=output/measures --output-format=csv.gz
    outputs:
      highly_sensitive:
        cohort: output/measures/input_*.csv.gz
  
  generate_study_population_infection:
    run: cohortextractor:latest generate_cohort --study-definition study_definition_infection_variables --index-date-range "2019-01-01 to today by month" --skip-existing --output-dir=output/measures --output-format=csv.gz
    outputs:
      highly_sensitive:
        cohort: output/measures/input_infection_*.csv.gz

  generate_study_population_elderly:
    run: cohortextractor:latest generate_cohort --study-definition study_definition_elderly 
      --output-format=csv.gz
    outputs:
      highly_sensitive:
        cohort: output/input_elderly.csv.gz

  generate_measures:
    run: cohortextractor:latest generate_measures --study-definition study_definition --skip-existing --output-dir=output/measures
    needs: [generate_study_population]
    outputs:
      moderately_sensitive:
        measure_csv: output/measures/measure_*.csv
  
  generate_measures_infection:
    run: cohortextractor:latest generate_measures --study-definition study_definition_infection_variables --skip-existing --output-dir=output/measures
    needs: [generate_study_population_infection]
    outputs:
      moderately_sensitive:
        measure_csv: output/measures/measure_infec_*.csv
        
  # describe_elderly_agedis:
  #   run: r:latest analysis/tables/gen_csv_age_check.R
  #   needs: [generate_study_population_elderly]
  #   outputs:
  #     moderately_sensitive:
  #       agetable: output/age_quant.csv

  # describe:
  #   run: r:latest analysis/plot/overall_ab_prescribing.R
  #   needs: [generate_measures]
  #   outputs:
  #     moderately_sensitive:
  #       cohort: output/overall.png
  #       boxplot: output/overallbox.png

  # describe_percentile:
  #   run: r:latest analysis/plot/overall_ab_prescribing_2575percentile.R
  #   needs: [generate_measures]
  #   outputs:
  #     moderately_sensitive:
  #       percentile: output/overall_25th_75th_percentile.png

  # describe_starpu:
  #   run: r:latest analysis/plot/starpu_ab_prescribing.R
  #   needs: [generate_measures]
  #   outputs:
  #     moderately_sensitive:
  #       cohort: output/starpuline.png
  #       boxplot: output/starpubox.png
  
  # generate_notebook_starpu:
  #   run: jupyter:latest jupyter nbconvert /workspace/analysis/starpu.ipynb --execute --to html --output-dir=/workspace/output --ExecutePreprocessor.timeout=86400
  #   needs: [generate_measures]
  #   outputs:
  #     moderately_sensitive:
  #       notebook: output/starpu.html 
  #       figures: output/*
  #       #tables: output/tables/*
  #       #csvs: output/*/* # two possible subfolders
  #       #text: output/text/*
  
  # describe_consultation_rate:
  #   run: r:latest analysis/plot/incident_consultation_age_stacked_barchart.R
  #   needs: [generate_measures]
  #   outputs:
  #      moderately_sensitive:
  #       bar1: output/consult_age_UTI.png
  #       bar2: output/consult_age_LRTI.png
  #       bar3: output/consult_age_URTI.png
  #       bar4: output/consult_age_sinusitis.png
  #       bar5: output/consult_age_ot_externa.png
  #       bar6: output/consult_age_otmedia.png
  #       bar7: output/consult_age_repeatedUTI.png
       


  # describe_consultation_prescribed:
  #   run: r:latest analysis/plot/consultation_prescibed_percentage.R
  #   needs: [generate_study_population]
  #   outputs:
  #      moderately_sensitive:
  #       bar1: output/prescribed_percentage_UTI.png
  #       csvs: output/uti_prescrib_check.csv
     

  #generate_notebook_starpu:
  #  run: jupyter:latest jupyter nbconvert /workspace/analysis/starpu.ipynb --execute --to html --output-dir=/workspace/output/hospitalisation_risk --ExecutePreprocessor.timeout=86400
  #  needs: [generate_measures]
  #  outputs:
  #    moderately_sensitive:
  #      notebook: output/hospitalisation_risk/starpu.html 
  #      figures: output/hospitalisation_risk/*
        #tables: output/tables/*
        #csvs: output/*/* # two possible subfolders
        #text: output/text/*

  # generate_notebook_hospitalisation_analysis:
  #   run: jupyter:latest jupyter nbconvert /workspace/analysis/hospitalisation_analysis.ipynb --execute --to html --output-dir=/workspace/output/hospitalisation_risk --ExecutePreprocessor.timeout=86400
  #   needs: [generate_study_population]
  #   outputs:
  #   moderately_sensitive:
  #       notebook: output/hospitalisation_risk/hospitalisation_analysis.html 
  #       figures: output/hospitalisation_risk/*
    
  # generate_notebook_hospitalisation_analysis:
  #   run: jupyter:latest jupyter nbconvert /workspace/analysis/hospitalisation_analysis.ipynb --execute --to html --output-dir=/workspace/output/hospitalisation_risk --ExecutePreprocessor.timeout=86400
  #   needs: [generate_study_population]
  #   outputs:
  #     moderately_sensitive:
  #       notebook: output/hospitalisation_risk/hospitalisation_analysis.html 
  #       figures: output/hospitalisation_risk/*

  # describe_prior_ab_12mb4:
  #   run: r:latest analysis/plot/ab_1yb4_stackedbar_2.R
  #   needs: [generate_study_population]
  #   outputs:
  #      moderately_sensitive:
  #       plot: output/AB_1yb4_line.jpeg
  #       plot_sex: output/AB_1yb4_SEX.jpeg
  #       count_table: output/prior_ab_by_month.csv      


  describe_consultation_rate_all:
    run: r:latest analysis/plot/incident_consultation_by_age_infection.R
    needs: [generate_measures_infection]
    outputs:
       moderately_sensitive:
        plot1: output/consult_age_1.jpeg
        plot2: output/consult_age_2.jpeg
        plot3: output/consult_all.jpeg
        csv1: output/consultation_rate.csv
        csv2: output/consultation_GP_rate.csv

  describe_infection_prescribed_percent:
    run: r:latest analysis/plot/infection_prescibed_percent.R
    needs: [generate_measures_infection]
    outputs:
       moderately_sensitive:
        plot1: output/infection_ab_precent_p1.jpeg
        plot2: output/infection_ab_precent_p2.jpeg
        plot3: output/infection_ab_precent_i1.jpeg
        plot4: output/infection_ab_precent_i2.jpeg
        plot5: output/infection_ab_precent_all.jpeg
        csv1: output/prescribed_infection_prevalent.csv
        csv2: output/prescribed_infection_incident.csv

  describe_top10ABtypes_byInfection:
    run: r:latest analysis/plot/abtypes_top10.R
    needs: [generate_measures_infection]
    outputs:
       moderately_sensitive:
        plot1: output/abtype_UTI.jpeg
        plot2: output/abtype_URTI.jpeg
        plot3: output/abtype_LRTI.jpeg
        plot4: output/abtype_sinusitis.jpeg
        plot5: output/abtype_ot_externa.jpeg
        plot6: output/abtype_otmedia.jpeg
        plot7: output/abtype_percent_UTI.jpeg
        plot8: output/abtype_percent_URTI.jpeg
        plot9: output/abtype_percent_LRTI.jpeg
        plot10: output/abtype_percent_sinusitis.jpeg
        plot11: output/abtype_percent_ot_externa.jpeg
        plot12: output/abtype_percent_otmedia.jpeg
        csv: output/abtype_top10_by_infection.csv
  
  # describe_top10ABtypes_total:
  #   run: r:latest analysis/plot/types_ab_prescriptions.R
  #   needs: [generate_study_population]
  #   outputs:
  #      moderately_sensitive:
  #       plot1: output/abtype_all_Rx.jpeg
  #       plot2: output/abtype_all_Rx_percent.jpeg

Timeline

  • Created:

  • Started:

  • Finished:

  • Runtime: 100:14:30

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

Job information

Status
Succeeded
Backend
TPP
Workspace
service_eval_work
Requested by
Vicki Palin UoM
Branch
main
Force run dependencies
No
Git commit hash
53389c6
Requested actions
  • generate_study_population

Code comparison

Compare the code used in this Job Request