Priming Primary Care Physicians to Treat Osteoporosis After a Fragility Fracture: An Integrated Multidisciplinary Approach
Bibliographic record
Abstract
OBJECTIVE: To evaluate 2 incremental levels of intervention designed to increase initiation of osteoporosis treatment by primary care physicians (PCP) following fragility fractures (FF). METHODS: Women and men over age 50 years were screened for incident FF in fracture clinics, and eligible outpatients were randomly assigned to standard care (SC) or to either minimal (MIN) or intensive (INT) interventions. The MIN and INT interventions were intended to educate and motivate both patients and PCP, but differed in their frequency of contact and information content. Delivery of osteoporosis medication was confirmed with pharmacists. Treatment rates were analyzed using an intention-to-treat approach. RESULTS: At inclusion, 74.3% of 881 outpatients with FF were untreated. Followup at 12 months was completed in 92.3% of patients. Up to 90% of patients treated at inclusion remained treated at 12 months. Among patients who initially were untreated, 18.8% in the SC group, 40.4% in the MIN, and 53.2% in the INT groups were treated at 12 months. Change in treatment rates (adjusted for age and initial treatment) increased significantly after both MIN and INT. Only the INT intervention significantly increased treatment rates in patients with previous fractures. Negative predictors of change in treatment status included non-major FF, age younger than 65 years, and male sex. CONCLUSION: Both interventions significantly increased initiation of osteoporosis treatment. Our multidisciplinary intervention builds on existing first-line structures and uses minimal specialized resources. Iterative and systematic interventions in the context of clinical care may modify the approach of PCP to osteoporosis management after FF and narrow the care gap in the long term.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".