Bone health post-stroke: a survey of stroke care physiatrists in Canada
Bibliographic record
Abstract
Objective: People who have experienced stroke are at a high risk for falls, fractures, and osteoporosis. Bone health post-stroke is often overlooked. The goal of this study was to understand current practice perspectives and barriers to bone health care post-stroke among physiatrists. Methods: We conducted an online survey of English-speaking stroke physiatrists practicing in Canada from October 2023 to April 2024. We recruited participants through the Canadian Association of Physical Medicine and Rehabilitation newsletter and direct contact via hospital or university email. The survey included demographic and multiple-choice questions as well as open-ended queries. Data were summarized using frequencies and percentages, and open-ended questions were assessed qualitatively for themes. Results: Twenty-two physiatrists completed the survey. Female physiatrists made up 45.5% of respondents, and 36.4% were in their first 5 years of practice. Most worked in an academic hospital (81.8%). The majority (81.9%) of respondents felt there is a need for post-stroke bone health guidelines. Important themes that emerged from open-ended questions included a lack of awareness, research, and resources. Conclusions: In this study of Canadian physiatrists, most respondents feel post-stroke bone health guidelines would be beneficial. More research and resources focused upon bone health in this population is needed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 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) | 0.003 | 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".