Validating a method that deals with missing drug information in the Saskatchewan Drug Plan database
Bibliographic record
Abstract
INTRODUCTION: An important limitation of Saskatchewan Drug Plan is the incomplete prescription data during an 18-month period (1987-1988), referred to here as the "black-hole". OBJECTIVE: To assess the impact of assuming drug non-exposure during the "black-hole" on measures of effect. METHODS: We used data from a matched case-control study carried out to assess the association between warfarin use and risk of prostate cancer. All subjects diagnosed with prostate cancer between 1981 and 2002 were matched to six controls. In order to avoid the "black-hole", we included subjects whose cancer was diagnosed after 1994. Conditional logistic regression was used to calculate adjusted incidence rates and 95% confidence intervals (CIs) of prostate cancer in relation to warfarin. Two analyses were carried out: (a) without a "black-hole" and (b) with a random "black-hole" of 18 months imposed in the drug history, during which time subjects were assumed to be unexposed. RESULTS: Compared to non-use, the OR of prostate cancer for ever-use of warfarin in the preceding 5 years was 0.91 (95%CI: 0.81-1.02) (without "black-hole") and 0.89 (95%CI: 0.79-1.01) (with "black-hole"). Compared to non-use, cumulative use of 1, 2, 3, and 4 years were associated with ORs of 1.02, 0.94, 0.77, and 0.76, respectively in the analyses without a "black-hole", and 1.02, 0.88, 0.75, and 0.76, respectively in the analyses with a "black-hole" imposed. CONCLUSION: When using Saskatchewan Drug Plan data, assuming non-exposure to warfarin during the "black-hole" has a minor effect on the measures of association.
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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 | 0.055 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".