Abstract TP237: Acute Risk Factors Associated With Health Service Use Within 90 Days Before Stroke: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: While traditional risk factors are chronic in nature, it is difficult to predict the risk of stroke with respect to time. Identifying certain acute risk factors, particularly associated with a health service contact, may help to prevent stroke. We performed a systematic review to understand the acute risk factors having health service contact within 90 days before incident strokes. Methods: PubMed, Embase and Scopus were searched for studies within last 10 years (2013- September 2022). Studies selected using PRISMA guidelines underwent quality assessment with Newcastle Ottawa scale. Three reviewers independently reviewed and included case control, case-crossover, self-controlled case series and cohort studies that reported raw data or estimates of association between acute events having healthcare contact within 90 days and stroke. Results: 106 studies across 14 countries were included from 12,628 records. Most studies reported all types of strokes as the outcome, with the most evaluated risk-periods being 30 and 90 days before stroke. Groups of acute risk factors were identified based on the reason for healthcare contact. Surgeries(n=43) and medical conditions(n=37) were the commonest groups identified for contacting health services in the acute period before stroke, followed by medications(n=14) and other reasons(n=12) including vaccinations and pathology tests. Subgroups were identified based on the type of acute risk factor (Figure 1). The most commonly examined acute risk factors included infections, myocardial infarction, cardiac surgeries, use of antipsychotics and influenza vaccines. Detailed meta-analyses summarizing the magnitude of effect under each sub-group will be presented. Conclusion: Potential acute risk factors having healthcare contact within 90 days preceding stroke have been identified. Further research to assess the role of interventions in health services addressing the risk of stroke from these events is warranted.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.044 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".