Quality and risk of bias assessment.
Bibliographic record
Abstract
<div><p>Indigenous Peoples across North America and Oceania experience worse health outcomes compared to non-Indigenous people, including increased post-operative mortality. Several gaps in data exist regarding global differences in surgical morbidity and mortality for Indigenous populations based on geographic locations and across surgical specialties. The aim of this study is to evaluate disparities in post-operative outcomes between Indigenous and non-Indigenous populations. This systematic review and meta-analysis was conducted in accordance with PRISMA and MOOSE guidelines. Eight electronic databases were searched with no language restriction. Studies reporting on Indigenous populations outside of Canada, the USA, New Zealand, or Australia, or on interventional procedures were excluded. Primary outcomes were post-operative morbidity and mortality. Secondary outcomes included reoperations, readmission rates, and length of hospital stay. The Newcastle Ottawa Scale was used for quality assessment. Eighty-four unique observational studies were included in this review. Of these, 67 studies were included in the meta-analysis (Oceania n = 31, North America n = 36). Extensive heterogeneity existed among studies and 50% were of poor quality. Indigenous patients had 1.26 times odds of post-operative morbidity (OR = 1.26, 95% CI: 1.10–1.44, p<0.01) and 1.34 times odds of post-operative infection (OR = 1.34, 95% CI: 1.12–1.59, p<0.01) than non-Indigenous patients. Indigenous patients also had 1.33 times odds of reoperation (OR = 1.33, 95% CI: 1.02–1.74, p = 0.04). In conclusion, we found that Indigenous patients in North American and Oceania experience significantly poorer surgical outcomes than their non-Indigenous counterparts. Additionally, there is a low proportion of high-quality research focusing on assessing surgical equity for Indigenous patients in these regions, despite multiple international and national calls to action for reconciliation and decolonization to improve quality surgical care for Indigenous populations.</p></div>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.056 | 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 teacher head, 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".