Association of Polypharmacy and Statin New-User Adherence in a Veterans Health Administration Population
Bibliographic record
Abstract
BACKGROUND: The relationship between multiple medication consumption and medication adherence is not well understood. OBJECTIVE: To determine the association between the number of active medications on the patient medication profile at baseline and adherence in new users of statins. METHODS: This was a retrospective cohort study of new users of statin medications from the Veterans Health Administration. We explored the correlation between the number of baseline medications and adherence, grouping patients by number of active medications on the study index date via Cochran-Armitage trend test and multiple linear regression. The adherence metric calculated for each patient was the medication possession ratio (MPR). Adherence was defined as achieving a 0.8 MPR or greater in primary analysis and a 0.9 MPR or greater in the secondary analysis. RESULTS: There was a statistically significant trend of increasing proportion of adherent participants as baseline medication count grew (P value < .001). The regression further demonstrated that statin MPR was increased by 0.04, 0.07, 0.10, and 0.14 for the 6 to 10 medication count, 11 to 15 medication count, 16 to 20 medication count, and >20 medication count groups, respectively, in comparison with the reference 1 to 5 medication count group (P < .001 for all comparisons). An MPR threshold of 0.9 provided consistent evidence of improved adherence as number of medications increased (P < .001). CONCLUSIONS: Increased medication count at baseline was associated with improved adherence for new users of statins.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".