Abstract 140: The Incidence Of Stroke In Indigenous Populations Of Countries With A Very High Human Development Index: A Systematic Review.
Bibliographic record
Abstract
Introduction: Despite known socioeconomic and health disparities affecting Indigenous populations in developed countries, stroke incidence data are sparse. With Indigenous Advisory Board oversight, we undertook a systematic review to compare Indigenous with non-Indigenous stroke incidence rates in countries with a very high Human Development Index (HDI). Methods: We identified population-based stroke incidence studies published from 1990-2022 in Indigenous adult populations of developed countries using PubMed, EMBASE and Global Health databases, without language restriction. We excluded non-peer-reviewed sources, studies with <10 Indigenous people, or studies not covering a 35-64 year minimum age range. Two reviewers independently screened titles, abstracts, and full texts, and extracted data. We assessed quality using "ideal" criteria for population-based stroke incidence studies, the Newcastle-Ottawa Scale for risk of bias, and CONSIDER criteria for Indigenous research. Results: Among 13,041 publications, 24 studies (19 full text, 5 abstracts) from 7 countries met inclusion criteria. Compared with respective non-Indigenous populations (Fig 1), age-standardised incidence rates were greater in Aboriginal and Torres Strait Islander Australians (ratios ranging from 1.7-3.2), American Indians (1.2), Sámi of Sweden/Norway (1.08-2.14), and Singaporean Malay (1.7-1.9), with higher rate ratios at younger ages. Studies had substantial heterogeneity in design and risk of bias. Few investigators reported Indigenous stakeholder involvement. Conclusions: In countries with a very high HDI, available data suggest marked disparities in stroke incidence in Indigenous populations, although there are gaps in data availability and quality. Indigenous stakeholder involvement in studies is infrequently reported. A greater understanding of stroke incidence in these populations is imperative for informing effective societal responses.
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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 | 0.015 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".