Job request: 15626
- Organisation:
- King's College London
- Workspace:
- gout
- ID:
- pavx5cuomvu2g2h7
This page shows the technical details of what happened when the authorised researcher Mark Russell 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
-
- Job identifier:
-
se6d2cpatoxgcr7x
-
- Job identifier:
-
j67kuu3haqgqgdql
Pipeline
Show project.yaml
version: '3.0'
expectations:
population_size: 100000
actions:
generate_study_population:
run: cohortextractor:latest generate_cohort --study-definition study_definition --output-format=csv.gz
outputs:
highly_sensitive:
cohort: output/input.csv.gz
generate_study_population_count:
run: cohortextractor:latest generate_cohort --study-definition study_definition_count --output-format=csv.gz
outputs:
highly_sensitive:
cohort: output/input_count.csv.gz
summary_counts:
run: stata-mp:latest analysis/002_summary_counts.do
needs: [generate_study_population_count]
outputs:
highly_sensitive:
log1: logs/summary_counts.log
generate_study_population_allpts:
run: cohortextractor:latest generate_cohort --study-definition study_definition_allpts --output-format=csv.gz
outputs:
highly_sensitive:
cohort: output/input_allpts.csv.gz
generate_study_population_2015:
run: cohortextractor:latest generate_cohort --study-definition study_definition_year --index-date-range "2015-07-01" --output-dir=output/measures --output-format=csv.gz
outputs:
highly_sensitive:
cohort: output/measures/input_year_2015-07-01.csv.gz
generate_study_population_2016:
run: cohortextractor:latest generate_cohort --study-definition study_definition_year --index-date-range "2016-07-01" --output-dir=output/measures --output-format=csv.gz
outputs:
highly_sensitive:
cohort: output/measures/input_year_2016-07-01.csv.gz
generate_study_population_2017:
run: cohortextractor:latest generate_cohort --study-definition study_definition_year --index-date-range "2017-07-01" --output-dir=output/measures --output-format=csv.gz
outputs:
highly_sensitive:
cohort: output/measures/input_year_2017-07-01.csv.gz
generate_study_population_2018:
run: cohortextractor:latest generate_cohort --study-definition study_definition_year --index-date-range "2018-07-01" --output-dir=output/measures --output-format=csv.gz
outputs:
highly_sensitive:
cohort: output/measures/input_year_2018-07-01.csv.gz
generate_study_population_2019:
run: cohortextractor:latest generate_cohort --study-definition study_definition_year --index-date-range "2019-07-01" --output-dir=output/measures --output-format=csv.gz
outputs:
highly_sensitive:
cohort: output/measures/input_year_2019-07-01.csv.gz
generate_study_population_2020:
run: cohortextractor:latest generate_cohort --study-definition study_definition_year --index-date-range "2020-07-01" --output-dir=output/measures --output-format=csv.gz
outputs:
highly_sensitive:
cohort: output/measures/input_year_2020-07-01.csv.gz
generate_study_population_2021:
run: cohortextractor:latest generate_cohort --study-definition study_definition_year --index-date-range "2021-07-01" --output-dir=output/measures --output-format=csv.gz
outputs:
highly_sensitive:
cohort: output/measures/input_year_2021-07-01.csv.gz
generate_study_population_2022:
run: cohortextractor:latest generate_cohort --study-definition study_definition_year --index-date-range "2022-07-01" --output-dir=output/measures --output-format=csv.gz
outputs:
highly_sensitive:
cohort: output/measures/input_year_2022-07-01.csv.gz
generate_measures:
run: cohortextractor:latest generate_measures --study-definition study_definition_year --output-dir=output/measures
needs: [generate_study_population_2015, generate_study_population_2016, generate_study_population_2017, generate_study_population_2018, generate_study_population_2019, generate_study_population_2020, generate_study_population_2021, generate_study_population_2022]
outputs:
moderately_sensitive:
measure_csv: output/measures/measure_*.csv
create_cohorts_allpts:
run: stata-mp:latest analysis/001_define_covariates_allpts.do
needs: [generate_study_population_allpts]
outputs:
highly_sensitive:
log1: logs/cleaning_dataset_allpts.log
data1: output/data/file_gout_allpts.dta
create_cohorts:
run: stata-mp:latest analysis/000_define_covariates.do
needs: [generate_study_population, generate_measures]
outputs:
highly_sensitive:
log1: logs/cleaning_dataset.log
data1: output/data/file_gout_all.dta
data2: output/data/gout_prevalence_sex_long.dta
data3: output/data/gout_incidence_sex_long.dta
data4: output/data/gout_admissions_sex_long.dta
run_baseline_tables_allpts:
run: stata-mp:latest analysis/101_baseline_characteristics_allpts.do
needs: [create_cohorts_allpts]
outputs:
moderately_sensitive:
log1: logs/descriptive_tables_allpts.log
doc1: output/tables/baseline_allpts.csv
run_baseline_tables:
run: stata-mp:latest analysis/100_baseline_characteristics.do
needs: [create_cohorts]
outputs:
moderately_sensitive:
log1: logs/descriptive_tables.log
doc1: output/tables/incidence_year_rounded.csv
doc2: output/tables/incidence_month_rounded.csv
doc3: output/tables/prevalance_year_rounded.csv
doc4: output/tables/incidence_admission_year_rounded.csv
doc6: output/tables/baseline_bydiagnosis.csv
doc7: output/tables/baseline_byyear.csv
doc8: output/tables/ult6m_byyear.csv
doc9: output/tables/ult6m_byregion.csv
doc10: output/tables/urate6m_byyear.csv
doc11: output/tables/urate6m_byregion.csv
figure2: output/figures/prevalance_year_rounded.svg
figure3: output/figures/incidence_admission_year_rounded.svg
run_itsa_models:
run: stata-mp:latest analysis/200_itsa_models.do
needs: [create_cohorts]
outputs:
moderately_sensitive:
log1: logs/itsa_models.log
figure1: output/figures/ITSA_ult_newey.svg
figure2: output/figures/ITSA_360_newey.svg
# doc1: output/tables/gp_to_appt_ITSA_table.csv
run_box_plots:
run: stata-mp:latest analysis/300_box_plots.do
needs: [create_cohorts]
outputs:
moderately_sensitive:
log1: logs/box_plots.log
figure 1: output/figures/regional_ult_overall.svg
figure 2: output/figures/regional_ult_2019.svg
figure 3: output/figures/regional_ult_2020.svg
figure 4: output/figures/regional_ult_2021.svg
figure 5: output/figures/regional_ult_2022.svg
figure 6: output/figures/regional_ult_merged.svg
# run_redacted_tables:
# run: stata-mp:latest analysis/400_redacted_tables.do
# needs: [create_cohorts]
# outputs:
# moderately_sensitive:
# log1: logs/redacted_tables.log
# doc1: output/tables/table_1_rounded_bydiag.csv
# doc2: output/tables/table_mean_bydiag_rounded.csv
# doc3: output/tables/table_median_bydiag_rounded.csv
# doc4: output/tables/table_median_bydiag_rounded_to21.csv
# doc5: output/tables/ITSA_tables_appt_delay_rounded.csv
# doc6: output/tables/ITSA_tables_csdmard_delay_rounded.csv
# doc7: output/tables/drug_byyearanddisease_rounded.csv
# doc8: output/tables/first_csdmard_rounded.csv
# doc9: output/tables/drug_byyearandregion_rounded.csv
# doc10: output/tables/referral_byregion_rounded.csv
# doc11: output/tables/consultation_medium_rounded.csv
# doc12: output/tables/table_median_bydiag_rounded_to21_report.csv
# doc13: output/tables/first_csdmard_rounded_report.csv
# run_redacted_tables_allpts:
# run: stata-mp:latest analysis/401_redacted_tables_allpts.do
# needs: [create_cohorts_allpts]
# outputs:
# moderately_sensitive:
# log1: logs/redacted_tables_allpts.log
# doc1: output/tables/table_1_rounded_allpts.csv
# convert_image_formats:
# run: python:latest python analysis/convert_images.py --input_dir output/figures --output_dir output/figures
# needs: [run_baseline_tables, run_itsa_models, run_box_plots, run_redacted_tables]
# outputs:
# moderately_sensitive:
# figures: output/figures/*.png
# generate_notebook:
# run: jupyter:latest jupyter nbconvert /workspace/analysis/report.ipynb --execute --to html --template basic --output-dir=/workspace/output --ExecutePreprocessor.timeout=86400 --no-input
# needs: [convert_image_formats,run_baseline_tables, run_itsa_models, run_box_plots, run_redacted_tables]
# outputs:
# moderately_sensitive:
# notebook: output/report.html
Timeline
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Created:
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Started:
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Finished:
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Runtime: 00:05:58
These timestamps are generated and stored using the UTC timezone on the TPP backend.
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