Postoperative Mortality of Indigenous Populations Compared With Nonindigenous Populations
Bibliographic record
Abstract
Importance: A range of factors have been identified as possible contributors to racial/ethnic differences in postoperative mortality that are also likely to hold true for indigenous populations. Yet despite its severity as an outcome, death in the period following a surgical procedure is underresearched for indigenous populations. Objective: To describe postoperative mortality experiences for minority indigenous populations compared with numerically dominant nonindigenous populations and examine the factors that drive any differences observed. Evidence Review: This review was conducted according to PRIMSA guidelines and registered on PROSPERO. Articles were identified through searches of the Embase, Ovid MEDLINE, Scopus, and Cumulative Index to Nursing and Allied Health Literature databases, with manual review of references and gray literature searches conducted. Eligible articles included those that reported associations between ethnicity/indigeneity and mortality up to 90 days following surgery and published in English between January 1, 1990, and March 26, 2019. Data on the study design, setting, participants (including indigeneity), and results were extracted. A modified Newcastle-Ottawa Quality Assessment Scale was used to determine study quality. Findings: A total of 442 abstracts were screened, 92 articles were reviewed in full text, and 21 articles (from 20 studies) and 7 reports underwent data extraction. All included studies were cohort studies (3 prospective and the remainder retrospective) investigating a wide range of surgical procedures in the US, Australia, or New Zealand. Seven studies were from single facilities, while the remainder used data from national databases. Sample sizes ranged, with indigenous sample sizes ranging from 20 to 3052 patients and a number of studies reporting less than 10 indigenous deaths. The postoperative mortality experience for minority indigenous populations compared with the nonindigenous populations was mixed. There was evidence from several studies that indigenous populations may be more likely to die following cardiac procedures. However, the available evidence has overall poor study quality, with methods to identify the indigenous populations being a major limitation of most of the studies. Conclusions and Relevance: Postoperative mortality experiences for indigenous populations should not be interpreted in isolation from the broader context of inequities across the health care pathway and must take into account the quality of data used for indigenous identification.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".