Osteoporosis Case Manager for Patients With Hip Fractures
Bibliographic record
Abstract
BACKGROUND: In a randomized trial of patients with hip fractures, we previously demonstrated that a hospital-based case manager could increase rates of appropriate osteoporosis treatment to 51% compared with 22% for usual care (P < .001). Alongside that trial, we conducted an economic analysis. METHODS: Patients with hip fractures were randomized to usual care (n = 110) or a case manager (n = 110) and followed up for 1 year. Time-motion studies were used to determine intervention costs. From a third-party health care payer perspective and over the patient's remaining lifetime, a Markov decision-analytic model was constructed to determine cost-effectiveness of the intervention compared with usual care. Costs and benefits were discounted at 3% and expressed in 2006 Canadian dollars. RESULTS: The intervention cost CaD $56 per patient. Compared with usual care, the intervention strategy was dominant: for every 100 patients case managed, 6 fractures (4 hip fractures) were prevented, 4 quality-adjusted life-years were gained, and CaD $260 000 was saved by the health care system. Irrespective of the number of patients case managed, the intervention reached a break-even threshold within 2 years. The intervention dominated usual care over the entire spectrum of 1-way sensitivity analyses and was cost-saving in 82% of probabilistic model simulations. CONCLUSIONS: Compared with usual care, we found that using a case manager for patients with hip fractures increased rates of appropriate osteoporosis treatment. The intervention dominated usual care, and the analysis suggests that systems implementing an intervention similar to ours should expect to see a reduction in fractures, gains in life expectancy, and substantial cost savings. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00175175.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".