Disparities in perioperative mortality outcomes between First Nations and non-First Nations peoples in Australia: protocol for a systematic review and planned meta-analysis
Bibliographic record
Abstract
BACKGROUND: Health inequities persist among First Nations people living in developed countries. Surgical care is pivotal in addressing a significant portion of the global disease burden. Evidence regarding surgical outcomes among First Nations people in Australia is limited. The perioperative mortality rate (POMR) indicates timely access to safe surgery and predicts long-term survival after major surgery. This systematic review will examine POMR among First Nations and non-First Nations peoples in Australia. METHODS: A systematic search strategy using MEDLINE, Embase, Emcare, Global Health, and Scopus will identify studies that include First Nations people and non-First Nations people who underwent a surgical intervention under anaesthesia in Australia. The primary focus will be on documenting perioperative mortality outcomes. Title and abstract screening and full-text review will be conducted by independent reviewers, followed by data extraction and bias assessment using the ROBINS-E tool. Meta-analysis will be considered if there is sufficient homogeneity between studies. The quality of cumulative evidence will be evaluated following the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) criteria. DISCUSSION: This protocol describes the comprehensive methodology for the proposed systematic review. Evaluating disparities in perioperative mortality rates between First Nations and non-First Nations people remains essential in shaping the discourse surrounding health equity, particularly in addressing the surgical burden of disease. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42021258970.
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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 | 0.095 | 0.138 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.021 | 0.031 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.069 | 0.009 |
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".