A Postfracture Initiative to Improve Osteoporosis Management in a Community Hospital in Ontario
Bibliographic record
Abstract
BACKGROUND: Screening programs to manage osteoporosis in fracture clinic environments have had varying success in terms of increasing rates of investigation and initiation of treatment for the disease. METHODS: We determined rates of postfracture investigation and care for osteoporosis in patients screened through a coordinator-based initiative in a community hospital fracture clinic. A coordinator screened outpatients, educated them about osteoporosis, advised them to see their family physician for assessment and/or treatment, and performed follow-up at six months. Men who were fifty years of age or older and women who were forty years of age or older and had a fragility fracture were eligible. RESULTS: Of 505 patients enrolled at baseline, 332 (66%) returned the follow-up questionnaire; 51% of those patients reported having had a bone mineral density test after screening and 26% had initiated first-line treatment (35% if the patients who had already initiated treatment at baseline were excluded) and an additional 23% were continuing treatment since baseline. After adjustment for demographic and baseline variables, patients who had initiated first-line treatment after screening were 4.15 times more likely to have had a bone mineral density test after screening than patients who had never initiated treatment and 11.67 times more likely to have had a bone mineral density test after screening than patients who had continued treatment since baseline. CONCLUSIONS: A coordinator-based osteoporosis screening program was associated with osteoporosis investigation and treatment. A postfracture bone mineral density test was highly associated with treatment initiation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".