Divergent Patterns of Antifracture Medication Use Following Fracture on Therapy: A Population-Based Cohort Study
Bibliographic record
Abstract
CONTEXT: Fracture on therapy should motivate better antifracture medication adherence. OBJECTIVE: This study aimed to describe osteoporosis medication adherence in women before and following a fracture. METHODS: This retrospective cohort analysis of antifracture medication possession ratios (MPR) among women in the Manitoba BMD Registry (1996-2013) included menopausal women who started antifracture drug therapy after a dual-energy x-ray absorptiometry (DXA)-BMD assessment with follow-up for 5 years during which a nontraumatic fracture occurred at least 1 year after starting treatment. Linked prescription records determined medication adherence (estimated by MPR) in 1-year intervals. The variable of interest was MPR in the year before and after the year in which the fracture occurred, with subgroup analyses according to duration of treatment pre-fracture. We chose an MPR of ≥ 0.50 to indicate minimum adherence needed for drug efficacy. RESULTS: There were 585 women with fracture on therapy, 193 (33%) had hip or vertebral fracture. Bisphosphonates accounted for 82.2% of therapies. Median MPR the year prior to fracture was 0.89 (IQR, 0.49-1.0) and 0.69 (IQR, 0.07-0.96) the year following the year of fracture (P < 0.0001). The percentage of women with MPR ≥ 0.5 pre-fracture was 73.8%, dropping to 57.3% post-fracture (P < 0.0001); when restricted to hip/vertebral fracture, results were similar (58.2% to 33.3%; P < 0.002). Among those with pre-fracture MPR < 0.5, only 21.7% achieved a post-fracture MPR ≥ 0.5. CONCLUSIONS: Although fracture on therapy may motivate sustained/improved adherence, MPR remains low or even declines after fracture in many. This could reflect natural decline in MPR with time but is paradoxical to expectations. Fracture on therapy represents an important opportunity for clinicians to reemphasize treatment adherence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".