Incidence and outcomes of critical illness in indigenous peoples: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Indigenous Peoples experience health inequities and racism across the continuum of health services. We performed a systematic review and meta-analysis of the incidence and outcomes of critical illness among Indigenous Peoples. Methods We searched Ovid MEDLINE/PubMed, Ovid EMBASE, Google Scholar, and Cochrane Central Register of Controlled Trials (inception to October 2022). Observational studies, case series of > 100 patients, clinical trial arms, and grey literature reports of Indigenous adults were eligible. We assessed risk of bias using the Newcastle–Ottawa Scale and appraised research quality from an Indigenous perspective using the Aboriginal and Torres Strait Islander Quality Assessment Tool. ICU mortality, ICU length of stay, and invasive mechanical ventilation (IMV) were compared using risk ratios and mean difference (MD) for dichotomous and continuous outcomes, respectively. ICU admission was synthesized descriptively. Results Fifteen studies (Australia and/or New Zealand [n = 12] and Canada [n = 3]) were included. Risk of bias was low in 10 studies and moderate in 5, and included studies had minimal incorporation of Indigenous perspectives or consultation. There was no difference in ICU mortality between Indigenous and non-Indigenous (RR 1.14, 95%CI 0.98 to 1.34, I2 = 87%). We observed a shorter ICU length of stay among Indigenous (MD − 0.25; 95%CI, − 0.49 to − 0.00; I2 = 95%) and a higher use for IMV among non-Indigenous (RR 1.10; 95%CI, 1.06 to 1.15; I2 = 81%). Conclusion Research on Indigenous Peoples experience with critical care is poorly characterized and has rarely included Indigenous perspectives. ICU mortality between Indigenous and non-Indigenous populations was similar, while there was a shorter ICU length of stay and less mechanical ventilation use among Indigenous patients. Systematic Review Registration PROSPERO CRD42021254661; Registered: 12 June, 2021.
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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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.032 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".