A Coordinator Program in Post-Fracture Osteoporosis Management Improves Outcomes and Saves Costs
Bibliographic record
Abstract
Background: The orthopaedic unit at a university teaching hospital hired an osteoporosis coordinator to identify patients with a fragility fracture and to coordinate their education, assessment, referral, and treatment of underlying osteoporosis. We report the results of an analysis of the cost-effectiveness of the use of a coordinator (in comparison with the use of no coordinator) in avoiding future costs of subsequent hip fracture. Methods: A one-year decision-analysis model was developed. The health outcome was subsequent hip fracture; only direct hospital costs were considered. With use of patient-level data from a previously described coordinator program and data from the literature, the expected annual incidence of subsequent hip fracture was calculated, on the basis of the type of index fracture (wrist, hip, humerus, other), attribution to osteoporosis, age, and gender. The rate of patient referral, the initiation of osteoporosis treatment, and adherence to therapy were modeled to modify the expected incidence of future hip fracture in the presence of a coordinator (with use of data from the program) and in the absence of a coordinator (with use of data from the literature). Sensitivity analysis modeling techniques were used to assess variable uncertainty and to evaluate coordinator cost-effectiveness. Results: Deterministic cost-effectiveness analysis showed that a tertiary care center that hired an osteoporosis coordinator who manages 500 patients with fragility fractures annually could reduce the number of subsequent hip fractures from thirty-four to thirty-one in the first year, with a net hospital cost savings of C$48,950 (Canadian dollars in
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".