Clinical Outcomes of Ischemic Stroke in Indigenous Populations: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: The burden and outcome of stroke in -Iindigenous populations is less well understood.This review evaluates ischemic stroke outcomes in Indigenous populations as compared to the general population in the context of recent advances in ischemic stroke therapy.Method: The OVID Medline and EMBASE databases were searched for this review.Clinical outcome was measured and compared using standardized outcome scale following stroke intervention in Indigenous as compared to non-Indigenous adult populations.Associated risk factors were also collected and compared.Results: 897 studies were identified, with 4 studies included in the final analysis.A total of (n=68895) patients were included who underwent thrombolysis.Study populations from Australia, New Zealand, United States, and Canada comprised of (n=2012) Indigenous patients.Mortality was significantly higher in Indigenous populations as compared to non-Indigenous (Odds Ratio-1.28,95% CI-1.12; 1.46).The odds ratios of atrial fibrillation (1.26, 95% CI-1.12;-1.41),diabetes (1.43, 95% CI-1.27; 1.62), hypertension (1.33, 95% CI-1.17; 1.51) and IHD (0.71, 95% CI-0.62; 0.81) in Indigenous patients was significantly higher than in non-Indigenous patients.Conclusion: Indigenous populations undergoing stroke therapy have significantly higher mortality compared to non-Indigenous populations.Comorbidities including diabetes, atrial fibrillation, and hypertension are more prevalent in Indigenous populations.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.022 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".