This page shows the technical details of what happened when authorised researcher Peter Inglesby requested one or more actions to be run against real patient data in the project, within a secure environment.
By cross-referencing the indicated Requested Actions with the
Pipeline section below, you can infer what
security level
various outputs were written to. Outputs marked as
highly_sensitive
can never be viewed directly by a researcher; 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
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- Job identifier:
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yyggopjytr5xurmg
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- Job identifier:
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rhyzjz4czwj24whs
Pipeline
Show project.yaml
version: '3.0'
expectations:
population_size: 500
actions:
generate_measure_cohort_1:
# week 45 2020 to 20 2020 inclusive
run: cohortextractor:latest generate_cohort --study-definition study_definition_measure --index-date-range "2020-11-02 to 2021-01-03 by week"
outputs:
highly_sensitive:
cohort: output/input_*.csv
generate_measure_cohort_2:
# week 51 2020 to 03 2021 inclusive
run: cohortextractor:latest generate_cohort --study-definition study_definition_measure --index-date-range "2021-01-04 to 2021-03-07 by week"
outputs:
highly_sensitive:
cohort: output/input*.csv
calculate_measures:
run: cohortextractor:latest generate_measures --study-definition study_definition_measure
needs: [generate_measure_cohort_1, generate_measure_cohort_2]
outputs:
highly_sensitive:
measure: output/measure_*_rate.csv
standardise_and_plot:
run: python:latest python analysis/time_series_plots.py
needs: [calculate_measures]
outputs:
moderately_sensitive:
tables: output/*_rate.csv
graph: output/time_series_plot.svg
generate_cohort:
run: cohortextractor:latest generate_cohort --study-definition study_definition_cohort
outputs:
highly_sensitive:
cohort: output/input_cohort.csv
count_by_strata:
run: python:latest python analysis/all_time_counts.py
needs: [generate_cohort]
outputs:
moderately_sensitive:
table: output/counts_table.csv
code_table: output/first_long_covid_code.csv
code_table2: output/all_long_covid_codes.csv
practice_summ: output/practice_summ.txt
Timeline
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Created:
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Started:
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Finished:
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Runtime: 00:00:06
These timestamps are generated and stored using the UTC timezone on the backend.
Job information
- Status
-
Failed
Job exited with an error code
- Backend
- EMIS
- Workspace
- long-covid-emis
- Requested by
- Peter Inglesby
- Branch
- EMIS
- Force run dependencies
- No
- Git commit hash
- 4a19c20
- Requested actions
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generate_cohort
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count_by_strata
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