Understanding fragility fracture patients’ decision-making process regarding bisphosphonate treatment
Bibliographic record
Abstract
We aimed to understand how patients 50 years and older decided to persist with or stop osteoporosis (OP) treatment. Processes related to persisting with or stopping OP treatments are complex and dynamic. The severity and risks and harms related to untreated clinical OP and the favorable benefit-to-risk profile for OP treatments should be reinforced. INTRODUCTION: Older adults with fragility fracture and clinical OP are at high risk of recurrent fracture, and treatment reduces this risk by 50 %. However, only 20 % of fracture patients are treated for OP and half stop treatment within 1 year. We aimed to understand how older patients with new fractures decided to persist with or stop OP treatment over 1 year. METHODS: We conducted a grounded theory study of patients 50 years and older with upper extremity fracture who started bisphosphonates and then reported persisting with or stopping treatment at 1 year. We used theoretical sampling to identify patients who could inform emerging concepts until data saturation was achieved and analyzed these data using constant comparison. RESULTS: We conducted 21 interviews with 12 patients. Three major themes emerged. First, patients perceived OP was not a serious health condition and considered its impact negligible. Second, persisters and stoppers differed in weighting the risks vs benefits of treatments, where persisters perceived less risk and more benefit. Persisters considered treatment "required" while stoppers often deemed treatment "optional." Third, patients could change treatment status even 1-year post-fracture because they re-evaluated severity and impact of OP vs risks and benefits of treatments over time. CONCLUSIONS: The processes and reasoning related to persisting with or stopping OP treatments post-fracture are complex and dynamic. Our findings suggest two areas of leverage for healthcare providers to reinforce to improve persistence: (1) the severity and risks and harms related to untreated clinical OP and (2) the favorable benefit-to-risk profile for OP treatments.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".