Abstract WP306: Low Rates of Screening and Treatment for Osteoporosis Following Stroke
Bibliographic record
Abstract
Introduction: Following stroke, bone mineral density (BMD) has been found to decline, particularly in the paretic extremity. Decreased BMD in combination with an increased risk of falls predisposes individuals to post-stroke fractures, which can result in serious disability. Despite this, current post-stroke best practice guidelines do not include recommendations on screening and treatment for osteoporosis or bone loss. The purpose of this study was to determine the frequency of BMD testing and prescribing of medications for treatment and prevention of osteoporosis following stroke. Hypothesis: We hypothesized that rates of screening and treatment for osteoporosis would be low following stroke. Methods: We performed a retrospective cohort study of patients aged over 20 years who were seen in the emergency department or hospitalized with stroke at any Ontario acute care institution between 2003 and 2012 and discharged alive. We identified BMD testing through the physician claims database and medication prescriptions in patients aged over 65 years using the Ontario Drug Benefits database. Results: Among 23,751 patients with stroke, only 4.8% of patients underwent BMD testing within 1 year and 15.5% of those aged over 65 were prescribed therapy for osteoporosis. In the 1,912 patients who sustained a low-trauma fracture within 2 years after stroke, BMD testing was conducted in only 9% and osteoporosis therapy was prescribed to 31.3% of those over 65 years. Conclusions: Following stroke, rates of testing and treatment for osteoporosis are very low, even in those with fractures. Due to the bone loss that may occur following stroke, it is recommended that high-risk individuals be targeted for BMD screening after stroke and treated accordingly.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".