Impact of Neighborhood Social Deprivation on Delays to Access for Transcatheter Aortic Valve Replacement: Wait‐Times and Clinical Consequences
Bibliographic record
Abstract
BACKGROUND: Transcatheter aortic valve replacement (TAVR) has become the standard of care for severe aortic stenosis treatment. Exponential growth in demand has led to prolonged wait times and adverse patient outcomes. Social marginalization may contribute to adverse outcomes. Our objective was to examine the association between different measures of neighborhood-level marginalization and patient outcomes while on the TAVR waiting list. A secondary objective was to understand if sex modifies this relationship. METHODS AND RESULTS: We conducted a population-based retrospective cohort study of 11 077 patients in Ontario, Canada, referred to TAVR from April 1, 2018, to March 31, 2022. Primary outcomes were death or hospitalization while on the TAVR wait-list. Using cause-specific Cox proportional hazards models, we evaluated the relationship between neighborhood-level measures of dependency, residential instability, material deprivation, and ethnic and racial concentration with primary outcomes as well as the interaction with sex. After multivariable adjustment, we found a significant relationship between individuals living in the most ethnically and racially concentrated areas (quintile 4 and 5) and mortality (hazard ratio [HR], 0.64 [95% CI, 0.47-0.88] and HR, 0.73 [95% CI, 0.53-1.00], respectively). There was no significant association between material deprivation, dependency, or residential instability with mortality. Women in the highest ethnic or racial concentration quintiles (4 and 5) had significantly lower risks for mortality (HR values of 0.52 and 0.56, respectively) compared with quintile 1. CONCLUSIONS: Higher neighborhood ethnic or racial concentration was associated with decreased risk for mortality, particular for women on the TAVR waiting list. Further research is needed to understand the drivers of this relationship.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".