Why Do Women Reject Bisphosphonates for Osteoporosis? A Videographic Study
Bibliographic record
Abstract
BACKGROUND: Despite access to effective, safe, and affordable treatment for osteoporosis, at-risk women may choose not to start bisphosphonate therapy. Understanding the reasons women give for rejecting a clinician's offer of treatment during consultations and how clinician's react to these reasons may help clinicians develop more effective strategies for fracture prevention and medication adherence. METHODS: We conducted a videographic evaluation of encounters in the Osteoporosis Choice randomized trial of a decision aid about bisphosphonates vs. usual primary care. Eligible videos involved consultations with women with an estimated 10-year fragility fracture risk >20% who verbalized at least one reason to not take bisphosphonates. Two reviewers independently reviewed eligible videos and verbatim transcripts, classifying patient views about bisphosphonate use, clinicians response to those views, and patient adherence at 6 months post visit. RESULTS: Eighteen video recordings (12 with decision aid) were eligible for analyses. We identified 37 reasons for and against bisphosphonate therapy. Eleven patients rejected treatment, offering 9 (average of 2 per patient) unique reasons against initiating bisphosphonates (most common: side effects 39% and distrust of medications in general 33%). When physicians conceded to patient views the outcome was no bisphosphonate use. Adherence to choices at 6 months was 100%. CONCLUSIONS: The expression of patient preferences is sometimes unfavorable to bisphosphonates treatment even among well-informed patients at high risk for osteoporotic fractures. At 6 months, patients who expressed concerns about these medicines behaved consistently with the decision made during the visit.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".