Abstract 13391: Exploring Prognostic Implications of Socioeconomic Status in Patients With Peripheral Arterial Disease
Bibliographic record
Abstract
Introduction: This systematic review sought to describe the prognostic implications of SES on clinical outcomes in patients undergoing vascular interventions for claudication and critical limb threatening ischemia (CLTI). Hypothesis: Methods: Studies were systematically searched across 5 databases from inception to June 2021. Studies focused on patients with claudication or CLTI undergoing open, endovascular, or hybrid procedures. Studies were included if SES was documented and associated with a clinical outcome. Two independent reviewers selected studies for inclusion, extracted data, and assessed risk of bias using ROBINS-I and Newcastle-Ottawa scales. Extracted data included study and clinical characteristics, demographics, interventions, outcome measured, and association of SES with the clinical outcomes. Results: Thirty four studies met our inclusion criteria and addressed the impact of SES in patients undergoing interventions for PAD, with 8 articles including only claudication patients, 2 including only CLTI patients, and 24 including a combination of both groups. The way SES was defined varied across studies, with some describing it in relation to insurance status (n=14), household income (n=13), income in area of living (n=8), income in the hospital’s area (n=1) and employment status (n=1). Low SES was associated with higher rates of amputation as a primary intervention, higher rates of above the knee amputation compared to below the knee amputation, higher 30 day postoperative death, longer lengths of stay, higher surgical sites infections, amputations following primary interventions, in-hospital complications, long term major adverse limb events and long term amputation rates. Given the amount of heterogeneity that was present in the designs, populations, and comparators among the included studies, we were unable to statistically pool data across trials. Conclusions: Our qualitative findings suggest that low SES is associated with significant adverse outcomes in patients with PAD including higher rates of primary amputation, post-operative mortality, length of stay, among others. Reasons for these disparities should be explored to identify solutions for decreasing and eliminating these health inequities.
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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 | 0.009 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".