Socioeconomic Status and Kidney Transplant Outcomes in a Universal Healthcare System: A Population-based Cohort Study
Bibliographic record
Abstract
BACKGROUND: Conflicting evidence exists regarding the relationship between socioeconomic status (SES) and outcomes after kidney transplantation. METHODS: We conducted a population-based cohort study in a publicly funded healthcare system using linked administrative healthcare databases from Ontario, Canada to assess the relationship between SES and total graft failure (ie, return to chronic dialysis, preemptive retransplantation, or death) in individuals who received their first kidney transplant between 2004 and 2014. Secondary outcomes included death-censored graft failure, death with a functioning graft, all-cause mortality, and all-cause hospitalization (post hoc outcome). RESULTS: Four thousand four hundred-fourteen kidney transplant recipients were included (median age, 53 years; 36.5% female), and the median (25th, 75th percentile) follow-up was 4.3 (2.1-7.1) years. In an unadjusted Cox proportional hazards model, each CAD $10000 increase in neighborhood median income was associated with an 8% decline in the rate of total graft failure (hazard ratio [HR], 0.92; 95% confidence interval [CI], 0.87-0.97). After adjusting for recipient, donor, and transplant characteristics, SES was not significantly associated with total or death-censored graft failure. However, each CAD $10000 increase in neighborhood median income remained associated with a decline in the rate of death with a functioning graft (adjusted (a)HR, 0.91; 95% CI, 0.83-0.98), all-cause mortality (aHR, 0.92; 95% CI, 0.86-0.99), and all-cause hospitalization (aHR, 0.95; 95% CI, 0.92-0.98). CONCLUSIONS: In conclusion, in a universal healthcare system, SES may not adversely influence graft health, but SES gradients may negatively impact other kidney transplant outcomes and could be used to identify patients at increased risk of death or hospitalization.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".