Post-Stroke Osteoporosis Screening: A Scoping Review
Bibliographic record
Abstract
INTRODUCTION: There are no specific screening recommendations for post-stroke osteoporosis despite evidence that people post-stroke are at heightened risk of fragility fractures. Our objective was to explore the extent of evidence and map the current literature available for osteoporosis screening in the post-stroke population. METHODS: This scoping review searched for articles in MEDLINE, Embase, and CINAHL databases published in English before May 2024, involving osteoporosis screening for adults after stroke. Title and abstract screening as well as full-text review and data extraction was performed by two reviewers. Analysis of the studies is descriptive and narrative. RESULTS: Eight articles met inclusion criteria: five published articles and three peer-reviewed conference abstracts. Three study designs were utilized: four cross-sectional studies, three cohort studies, and one survey. Four studies investigated post-stroke osteoporosis screening rates, two looked at screening pathways for post-stroke osteoporosis, and two assessed novel osteoporosis screening tools. No post-stroke osteoporosis screening guidelines were found. Across all included studies, reported screening rates for post-stroke osteoporosis were less than 10%. CONCLUSIONS: This scoping review emphasizes the need for osteoporosis screening guidelines and risk assessment tools specific to the post-stroke population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".