Ethical issues in pharmacoepidemiologic research using Saskatchewan administrative health care utilization data
Bibliographic record
Abstract
PURPOSE: To describe the process of obtaining access to the administrative health care utilization data of the Canadian province of Saskatchewan and the ethical issues involved. METHODS: The report focuses on the process of obtaining data for two recent studies. In the first, associations between aplastic anemia and agranulocytosis and prior drug use were evaluated, while the second is an examination of anti-arrhythmia drug utilization. In these studies, data from files containing computerized information on prescription drug use, hospitalizations, physician services and cancer registrations were linked together and also with information from hospital charts, physician records and death registrations. RESULTS: Data on individual patients are available from the Saskatchewan data-files after the removal of identifying variables, and access to external information from hospitals, physicians, death registrations and the patients themselves is possible. However, researchers must accept that data considered to be only indirectly relevant to the objectives of the study or which, due to small numbers, may potentially identify either patients or physicians will only be released in aggregate form. CONCLUSIONS: Access to the Saskatchewan data-files and to external information from hospitals, physicians and death registrations is normally straightforward. Restrictions that are applied are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.465 | 0.467 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".