Cost Analysis of a Fracture Liaison Service: A Prospective Study for Secondary Prevention After Fractures of the Hip
Bibliographic record
Abstract
Background: Fracture liaison services (FLSs) have proven to be effective in treating osteoporosis associated with fragility fractures. For patients with fragility fractures of the hip, FLS programs are expected to be cost-effective because of the high risk of re-fracture and the high cost of fracture treatment. In this study, we evaluated the essential factors in determining whether the FLS saves or loses more than it costs. Methods: A prospective-randomized study was done in patients with hip fragility fractures using a hospital-based FLS program in parallel with a cost analysis. Data were generated from a cohort of patients using actual data for FLS effectiveness, individual costs of hip fracture treatment, and medication costs based on an accepted treatment algorithm. Results: There were 200 patients randomized and 180 analyzed for costs. Results showed that the cost-benefit of the FLS was dependent on the medication used for osteoporosis. Specifically, using the medication algorithm in this study, the loss per patient enrolled in the FLS was $671 for a 2-year period. If intravenous zoledronic acid had been used, then the loss would have been $221. If only oral bisphosphonates had been used, then the FLS would have saved $109 per patient for a 2-year period. Conclusions: The analysis done here shows that medication cost is the critical component in cost-effectiveness of an FLS program. Additional work needs to be done refining the medication algorithm considering medication costs but individualized to patient needs based on fracture risk. J Curr Surg. 2022;12(2):29-37 doi: https://doi.org/10.14740/jcs460
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".