Measuring and improving adherence to osteoporosis pharmacotherapy
Bibliographic record
Abstract
PURPOSE OF REVIEW: Osteoporosis is a major public health issue resulting in considerable fracture-related morbidity. Although effective treatment exists, adherence to osteoporosis pharmacotherapy is suboptimal and linked to reduced drug effectiveness. In this paper, we review methods of measuring and improving adherence to osteoporosis pharmacotherapy. RECENT FINDINGS: Most patients will stop osteoporosis pharmacotherapy, yet the majority who discontinue will reinitiate treatment after an extended gap. The key to improving adherence to osteoporosis pharmacotherapy is to reduce the number and length of gaps in treatment. Multifaceted and individualized interventions may help to improve adherence. New strategies aimed at identifying patients likely to stop therapy may also facilitate the development of targeted interventions. SUMMARY: Adherence to osteoporosis pharmacotherapy is suboptimal with short periods of persistence and lengthy gaps in therapy. Regular communication regarding the importance of continued therapy is critical. More research to help identify risk profiles of patients likely to become nonadherent, targeted multifaceted interventions to maximize adherence to therapy, and data to support when patients may safely consider a physician directed drug holiday is needed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".