Screening and Treatment for Osteoporosis After Stroke
Bibliographic record
Abstract
Background and Purpose- Stroke is a risk factor for subsequent osteoporosis and fractures. We sought to understand current rates and predictors of screening and treatment for bone loss after stroke. Methods- Using the Ontario Stroke Registry from July 1, 2003 to March 31, 2013, we identified patients ≥65 years who were seen in the emergency department or hospitalized with stroke at 11 regional stroke centers in Ontario, Canada and discharged alive. We calculated the cumulative incidence of (1) screening with bone mineral density testing and (2) treatment with medications for fracture prevention, within 1 year after the index stroke, accounting for the competing risk of death. We then used cause-specific hazard models to estimate the effect of various covariates on the cause-specific hazard of bone mineral density testing and osteoporosis pharmacotherapy. Results- In the sample of 16 581 patients, 5.1% overall and 2.9% of those without prior testing underwent screening bone mineral density testing, and 15.5% overall and 3.2% of those not previously on treatment were prescribed medications for fracture prevention within 1 year after stroke. Results were similar in all subgroups of patients. Female sex, prestroke osteoporosis, and poststroke falls and fractures were associated with increased rates of osteoporosis pharmacotherapy. Conclusions- Patients with recent stroke are infrequently screened and treated for osteoporosis, which may increase the risk of fractures. Future work should focus on identifying and treating patients who are at increased risk of fractures after stroke.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".