Job request: 16834
- Organisation:
- The London School of Hygiene & Tropical Medicine
- Workspace:
- healthcare_utilisation_openprompt
- ID:
- s477iwtv7fj2wwfp
This page shows the technical details of what happened when the authorised researcher Liang-Yu Lin 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:
-
ihw3pxu5xtoeeddn
Pipeline
Show project.yaml
version: '3.0'
expectations:
population_size: 500
actions:
generate_long_covid_exposure_dataset:
run:
databuilder:v0 generate-dataset
analysis/dataset_definition_unmatched_exp_lc.py
--output output/dataset_exp_lc_unmatched.csv
outputs:
highly_sensitive:
cohort: output/dataset_exp_lc_unmatched.csv
check_stp_regions:
needs: [generate_long_covid_exposure_dataset]
run: r:latest analysis/dm00_check_stp_regions.R
outputs:
moderately_sensitive:
stp_region: output/stp_regions_counts.csv
generate_list_gp_use_long_covid_dx:
run:
databuilder:v0 generate-dataset
analysis/dataset_definition_lc_gp_list.py
--output output/dataset_lc_gp_list.csv
outputs:
highly_sensitive:
cohort: output/dataset_lc_gp_list.csv
generate_dataset_comparator_exclude_gp_no_long_covid:
needs: [generate_list_gp_use_long_covid_dx]
run:
databuilder:v0 generate-dataset
analysis/dataset_definition_unmatched_comparator.py
--output output/dataset_comparator_unmatched.csv
outputs:
highly_sensitive:
cohort: output/dataset_comparator_unmatched.csv
split_unmatched_data_by_stp_regions:
needs: [generate_long_covid_exposure_dataset, generate_dataset_comparator_exclude_gp_no_long_covid]
run: r:latest analysis/dm00_split_stp_for_matching.R
outputs:
moderately_sensitive:
stp_exp_table: output/exp_stp_names_numbers.csv
stp_com_table: output/com_stp_names_numbers.csv
# highly_sensitive:
# exp_stp_1: output/exp_stp_E84000005.csv
# exp_stp_2: output/exp_stp_E84000006.csv
# exp_stp_3: output/exp_stp_E84000007.csv
# exp_stp_4: output/exp_stp_E84000008.csv
# exp_stp_5: output/exp_stp_E84000009.csv
# exp_stp_6: output/exp_stp_E84000010.csv
# exp_stp_7: output/exp_stp_E84000012.csv
# exp_stp_8: output/exp_stp_E84000013.csv
# exp_stp_9: output/exp_stp_E84000014.csv
# exp_stp_10: output/exp_stp_E84000015.csv
# exp_stp_11: output/exp_stp_E84000016.csv
# exp_stp_12: output/exp_stp_E84000017.csv
# exp_stp_13: output/exp_stp_E84000020.csv
# exp_stp_14: output/exp_stp_E84000021.csv
# exp_stp_15: output/exp_stp_E84000022.csv
# exp_stp_16: output/exp_stp_E84000023.csv
# exp_stp_17: output/exp_stp_E84000024.csv
# exp_stp_18: output/exp_stp_E84000025.csv
# exp_stp_19: output/exp_stp_E84000026.csv
# exp_stp_20: output/exp_stp_E84000027.csv
# exp_stp_21: output/exp_stp_E84000029.csv
# exp_stp_22: output/exp_stp_E84000033.csv
# exp_stp_23: output/exp_stp_E84000035.csv
# exp_stp_24: output/exp_stp_E84000036.csv
# exp_stp_25: output/exp_stp_E84000037.csv
# exp_stp_26: output/exp_stp_E84000040.csv
# exp_stp_27: output/exp_stp_E84000041.csv
# exp_stp_28: output/exp_stp_E84000042.csv
# exp_stp_29: output/exp_stp_E84000043.csv
# exp_stp_30: output/exp_stp_E84000044.csv
# exp_stp_31: output/exp_stp_E84000049.csv
# com_stp_1: output/comp_stp_E84000005.csv
# com_stp_2: output/comp_stp_E84000006.csv
# com_stp_3: output/comp_stp_E84000007.csv
# com_stp_4: output/comp_stp_E84000008.csv
# com_stp_5: output/comp_stp_E84000009.csv
# com_stp_6: output/comp_stp_E84000010.csv
# com_stp_7: output/comp_stp_E84000012.csv
# com_stp_8: output/comp_stp_E84000013.csv
# com_stp_9: output/comp_stp_E84000014.csv
# com_stp_10: output/comp_stp_E84000015.csv
# com_stp_11: output/comp_stp_E84000016.csv
# com_stp_12: output/comp_stp_E84000017.csv
# com_stp_13: output/comp_stp_E84000020.csv
# com_stp_14: output/comp_stp_E84000021.csv
# com_stp_15: output/comp_stp_E84000022.csv
# com_stp_16: output/comp_stp_E84000023.csv
# com_stp_17: output/comp_stp_E84000024.csv
# com_stp_18: output/comp_stp_E84000025.csv
# com_stp_19: output/comp_stp_E84000026.csv
# com_stp_20: output/comp_stp_E84000027.csv
# com_stp_21: output/comp_stp_E84000029.csv
# com_stp_22: output/comp_stp_E84000033.csv
# com_stp_23: output/comp_stp_E84000035.csv
# com_stp_24: output/comp_stp_E84000036.csv
# com_stp_25: output/comp_stp_E84000037.csv
# com_stp_26: output/comp_stp_E84000040.csv
# com_stp_27: output/comp_stp_E84000041.csv
# com_stp_28: output/comp_stp_E84000042.csv
# com_stp_29: output/comp_stp_E84000043.csv
# com_stp_30: output/comp_stp_E84000044.csv
# com_stp_31: output/comp_stp_E84000049.csv
# match_comparators:
# run:
# python:latest python analysis/match.py
# needs: [generate_dataset_comparator_exclude_gp_no_long_covid, generate_long_covid_exposure_dataset]
# outputs:
# highly_sensitive:
# matched_cases: output/matched_cases.csv
# matched_matches: output/matched_matches.csv
# matched_all: output/matched_combined.csv
# moderately_sensitive:
# matching_report: output/matching_report.txt
Timeline
-
Created:
-
Finished:
-
Runtime:
These timestamps are generated and stored using the UTC timezone on the TPP backend.
Job information
- Status
-
Failed
- Backend
- TPP
- Workspace
- healthcare_utilisation_openprompt
- Requested by
- Liang-Yu Lin
- Branch
- main
- Force run dependencies
- No
- Git commit hash
- b12ea87
- Requested actions
-
-
split_unmatched_data_by_stp_regions
-
Code comparison
Compare the code used in this Job Request