Comparability of self-reported medication use and pharmacy claims data.
Bibliographic record
Abstract
BACKGROUND: Many studies of medicine use rely on self-reports. Based on pharmacy claims data, this analysis tests whether such self-reports constitute a valid and reliable data source. DATA AND METHODS: Linked data from the Canadian Community Health Survey and the Ontario Drug Benefit Program were used to estimate the agreement, based on kappa statistics, between seniors' self-reported medication use and the claims data. Health, demographic and socio-economic factors associated with the likelihood of agreement were modeled with logistic regression. RESULTS: The prevalence of antihypertensive medication use among Ontario residents aged 65 or older was about 40% in 2001, based on both self-report and pharmacy claims, and in 2005, it was 52% for self-report and 49% based on claims data. The prevalence of oral diabetes medication use was comparable between the two data sources. Overall agreement between self-reported and claims data was "good" to "very good" for oral diabetes medications (kappa = 0.79 in 2001; 0.87 in 2005), but "moderate" for antihypertensive medications (kappa = 0.46 in 2001; 0.55 in 2005). Agreement improved somewhat from 2001 to 2005, with implementation of a more targeted survey question. INTERPRETATION: Self-reports appear to be an accurate data source for measuring medication use; however, for antihypertensive medications, self-reports by the oldest and sickest subpopulations should be used cautiously.
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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 | 0.071 | 0.242 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".