Is ethnicity an appropriate measure of health care marginalization? A systematic review and meta-analysis of the outcomes of diabetic foot ulceration in Aboriginal populations
Bibliographic record
Abstract
BACKGROUND: Aboriginal people have higher prevalence rates of diabetes than non-Aboriginal people in the same geographic locations, and diabetic foot ulcer (DFU) complication rates are also presumed to be higher. The aim of this systematic review and meta-analysis was to compare DFU outcomes in Aboriginal and non-Aboriginal populations. METHODS: We searched PubMed, Embase, CINAHL and the Cochrane Library from inception to October 2018. Inclusion criteria were all types of studies comparing the outcomes of Aboriginal and non-Aboriginal patients with DFU, and studies from Canada, the United States, Australia and New Zealand. Exclusion criteria were patient age younger than 18 years, and studies in any language other than English. The primary outcome was the major amputation rate. We assessed the risk of bias using the ROBINS-I (Risk Of Bias In Non-randomized Studies - of Interventions) tool. Effect measures were reported as odds ratio (OR) with 95% confidence interval (CI). RESULTS: Six cohort studies with a total of 244 792 patients (2609 Aboriginal, 242 183 non-Aboriginal) with DFUs were included. The Aboriginal population was found to have a higher rate of major amputation than the non-Aboriginal population (OR 1.85, 95% CI 1.04-3.31). Four studies were deemed to have moderate risk of bias, and 2 were deemed to have serious risk of bias. CONCLUSION: Our analysis of the available studies supports the conclusion that DFU outcomes, particularly the major amputation rate, are worse in Aboriginal populations than in non-Aboriginal populations in the same geographic locations. Rurality was not uniformly accounted for in all included studies, which may affect how these outcome differences are interpreted. The effect of rurality may be closely intertwined with ethnicity, resulting in worse outcomes.
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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 | 0.038 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.036 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".