Postoperative morbidity and mortality in pediatric indigenous populations: a scoping review and meta-analysis
Bibliographic record
Abstract
Mounting evidence suggests that childhood health is an important predictor of wellness as an adult. Indigenous peoples worldwide suffer worse health outcomes compared to settler populations. No study comprehensively evaluates surgical outcomes for Indigenous pediatric patients. This review evaluates inequities between Indigenous and non-Indigenous children globally for postoperative complications, morbidities, and mortality. Nine databases were searched for relevant subject headings including "pediatric", "Indigenous", "postoperative", "complications", and related terms. Main outcomes included postoperative complications, mortality, reoperations, and hospital readmission. A random-effects model was used for statistical analysis. The Newcastle Ottawa Scale was used for quality assessment. Fourteen studies were included in this review, and 12 met inclusion criteria for meta-analysis, representing 4793 Indigenous and 83,592 non-Indigenous patients. Indigenous pediatric patients had a greater than twofold overall (OR 2.0.6, 95% CI 1.23-3.46) and 30-day postoperative mortality (OR 2.23, 95% CI 1.23-4.05) than non-Indigenous populations. Surgical site infections (OR 1.05, 95% CI 0.73-1.50), reoperations (OR 0.75, 95% CI 0.51-1.11), and length of hospital stay (SMD = 0.55, 95% CI - 0.55-1.65) were similar between the two groups. There was a non-significant increase in hospital readmissions (OR 6.09, 95% CI 0.32-116.41, p = 0.23) and overall morbidity (OR 1.13, 95% CI 0.91-1.40) for Indigenous children. Indigenous children worldwide experience increased postoperative mortality. It is necessary to collaborate with Indigenous communities to promote solutions for more equitable and culturally appropriate pediatric surgical care.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.031 |
| Bibliometrics | 0.009 | 0.009 |
| 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) | 0.004 | 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".