Use of a Case Manager to Improve Osteoporosis Treatment After Hip Fracture
Bibliographic record
Abstract
BACKGROUND: Patients who survive hip fracture are at high risk of recurrent fractures, but rates of osteoporosis treatment 1 year after sustaining a fracture are less than 10% to 20%. We have developed an osteoporosis case manager intervention. The case manager educated patients, arranged bone mineral density tests, provided prescriptions, and communicated with primary care physicians. The intervention was compared with usual care in a randomized controlled trial. METHODS: We recruited from all hospitals that participate in the Capital Health system (Alberta, Canada), including patients 50 years or older who had sustained a hip fracture and excluding those who were receiving osteoporosis treatment or who lived in a long-term care facility. Primary outcome was bisphosphonate therapy 6 months after fracture; secondary outcomes included bone mineral density testing, appropriate care (bone mineral density testing and treatment if bone mass was low), and intervention costs. RESULTS: We screened 2219 patients and allocated 220, as follows: 110 to the intervention group and 110 to the control group. Median age was 74 years, 60% were women, and 37% reported having had previous fractures. Six months after hip fracture, 56 patients in the intervention group (51%) were receiving bisphosphonate therapy compared with 24 patients in the control group (22%) (adjusted odds ratio, 4.7; 95% confidence interval, 2.4-8.9; P < .001). Bone mineral density tests were performed in 88 patients in the intervention group (80%) vs 32 patients in the control group (29%) (P < .001). Of the 120 patients who underwent bone mineral density testing, 25 (21%) had normal bone mass. Patients in the intervention group were more likely to receive appropriate care than were patients in the control group (67% vs 26%; P < .001). The average intervention cost was $50.00 per patient. CONCLUSION: For a modest cost, a case manager was able to substantially increase rates of osteoporosis treatment in a vulnerable elderly population at high risk of future fractures.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".