Pharmacy Record Registration of Acetyl Salicylic Acid (ASA) Prescriptions in Quebec
Bibliographic record
Abstract
PURPOSE: To determine the extent of which acetyl salicylic acid (ASA) use is included in patients' pharmacy records. METHODS: During an in-home interview, people aged ≥ 65 years were asked to report all of the medications they had used at least once, including over-the-counter drugs, during the preceding month. Researchers recorded information on the drug name, reason for its use, and whether a physician prescribed it. From the pharmacy records, the drug names, prescription fill dates, quantities supplied, and the numbers of days' supply were recorded. The level of agreement for ASA use across data sources was assessed using proportions of agreement and kappa coefficients. RESULTS: Of 193 individuals interviewed, 86 reported the use of ASA, including 76 ASA users (88.4%) who said it was prescribed by a physician. Pharmacy medication records indicated that there were 74 users of ASA. The proportion of agreement for ASA use was 93.8%, and kappa coefficient was 0.87 (95% confidence interval: 0.80-0.94). The sensitivity, specificity, and positive predictive value of the pharmacy data were all high. CONCLUSIONS: A large proportion of ASA use is documented in pharmacy records in Quebec. Thus, the information regarding ASA use in pharmacy records is reliable. This result may not be reproducible in other settings where pharmaceutical reimbursement rules are different.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".