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This page shows the technical details of what happened when authorised researcher Milan Wiedemann requested one or more actions to be run against real patient data in the Blood Pressure - Quality and Outcomes Framework (QOF) 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
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.
Show Hide project.yaml
version: '3.0' expectations: population_size: 10000 actions: generate_study_population: run: cohortextractor:latest generate_cohort --study-definition study_definition --index-date-range "2019-09-01 to 2022-03-31 by month" --output-dir=output outputs: highly_sensitive: cohort: output/input_*.csv generate_study_population_ethnicity: run: cohortextractor:latest generate_cohort --study-definition study_definition_ethnicity --output-dir=output outputs: highly_sensitive: cohort: output/input_ethnicity.csv join_ethnicity: run: python:latest python analysis/join_ethnicity.py needs: [generate_study_population, generate_study_population_ethnicity] outputs: highly_sensitive: cohort: output/input*.csv generate_study_population_practice_count: run: cohortextractor:latest generate_cohort --study-definition study_definition_practice_count --index-date-range "2019-09-01 to 2022-03-31 by month" --output-dir=output outputs: highly_sensitive: cohort: output/input_practice_count_*.csv generate_measures: run: cohortextractor:latest generate_measures --study-definition study_definition --output-dir=output needs: [join_ethnicity] outputs: moderately_sensitive: measure_csv: output/measure_*_rate.csv calculate_rates: run: python:latest python analysis/rate_calculations.py needs: [generate_measures] outputs: moderately_sensitive: tables: output/rate_table_*.csv child_code_table: output/child_code_table.csv plots: output/plot_*.png decile_chart: output/decile_chart.png # Join all measure files for each indicator join_measures: run: r:latest analysis/join_measures.R needs: [calculate_rates] outputs: moderately_sensitive: measure_csv: output/measures/measures_bp002.csv generate_notebook: run: jupyter:latest jupyter nbconvert /workspace/analysis/qof_notebook.ipynb --execute --to html --output-dir=/workspace/output --ExecutePreprocessor.timeout=86400 --no-input needs: [calculate_rates, generate_study_population_practice_count] outputs: moderately_sensitive: notebook: output/qof_notebook.html run_tests: run: python:latest python -m pytest --junit-xml=output/pytest.xml --verbose outputs: moderately_sensitive: log: output/pytest.xml
State is inferred from the related Jobs.
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