The Complex Relation between Bisphosphonate Adherence and Fracture Reduction
Bibliographic record
Abstract
CONTEXT: Real-world adherence to bisphosphonate therapy is poor. Consistent data support a relation between medication adherence and fracture reduction, but relatively little attention has been paid to the effect of the method used to measure adherence on this relation or on the relation between adherence and specific fracture types. OBJECTIVE: Our objective was to assess the relation between bisphosphonate adherence and the risk of hip, vertebral, distal forearm, and any fracture using different measures of adherence. DESIGN: We conducted a cohort study using administrative claims data. Adherence was assessed in sequential 60-d periods. In models incorporating time-varying measures of adherence, the adjusted relation between adherence and fracture was examined using several methods for calculating the proportion of days covered (PDC). PATIENTS: Patients included community-dwelling elderly enrolled in a Pennsylvania pharmaceutical assistance program and Medicare initiating an oral bisphosphonate for osteoporosis. MAIN OUTCOME MEASURES: Risk of hip, vertebral, distal forearm, and any osteoporotic fracture was assessed. RESULTS: Fractures occurred at a rate of 43 per 1000 person-years among the 19,987 patients meeting study eligibility criteria. There was an inverse relation between adherence and fracture rate for all adherence measures and fracture types, excluding distal forearm fractures. High (80-100%) cumulative PDC was associated with a 22% reduction in overall fracture rate, a 23% reduction in hip fracture rate, and 26% reduction in vertebral fracture rate. CONCLUSIONS: We found a consistent relation between adherence with osteoporosis treatment and fracture reduction, regardless of method for measuring PDC. The similarity in results across adherence measures is likely due to the high correlation between them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".