Exploring Prognostic Implications of Race and Ethnicity in Patients With Peripheral Arterial Disease
Bibliographic record
Abstract
INTRODUCTION: Significant health inequalities in major adverse limb events exist. Ethnically minoritized groups are more prone to have a major adverse event following peripheral vascular interventions. This systematic review and meta-analysis aimed to describe the postoperative implications of racial and ethnic status on clinical outcomes following vascular interventions for claudication and chronic limb-threatening ischemia. METHODS: Searches were conducted across seven databases from inception to June 2021 and were updated in October 2022 to identify studies reporting claudication or chronic limb-threatening ischemia in patients who underwent open, endovascular, or hybrid procedures. Studies with documented racial and ethnic status and associated clinical outcomes were selected. Extracted data included demographic and clinical characteristics, vascular interventions, and measured outcomes associated with race or ethnicity. Meta-analyses were performed using random-effect models to report pooled odds ratios (ORs) with 95% confidence intervals (CIs). RESULTS: Seventeen studies evaluating the impact of Black versus White patients undergoing amputation as a primary intervention were combined in a meta-analysis, revealing that Black patients had a higher incidence of amputations as a primary intervention than White patients (OR: 1.91, 95% CI: 1.61-2.27). Another meta-analysis demonstrated that Black patients had significantly higher rates of amputation after revascularization (OR: 1.56, 95% CI: 1.28-1.89). Furthermore, multiple trends were demonstrated in the secondary outcomes evaluated. CONCLUSIONS: Our findings suggest that Black patients undergo primary major amputation at a significantly higher rate than White patients, with similar trends seen among Hispanic and First Nations patients. Black patients are also significantly more likely to be subjected to amputation following attempts at revascularization when compared to White patients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".