The influence of socioeconomic status on patient survival on chronic dialysis
Bibliographic record
Abstract
Socioeconomic status (SES) has been linked to worse end-stage kidney disease survival. The effect of SES on survival on chronic dialysis, including the impact of transplantation, was examined. A retrospective, observational study investigated the association of SES with dialysis patient survival, with censoring at time of transplantation. Adult patients commencing dialysis from 1990 to 2009 in an Irish tertiary center received a spatial SES score using the 2011 Pobal Haase-Pratschke Deprivation Index and were compared by quartile. Cox proportional hazard models and Kaplan-Meier survival analysis examined any association of SES with survival. The 1794 patients included had a median follow-up of 3.8 years. Patients in the lowest SES area quartile were significantly younger than the highest, mean age 56.7 vs. 59 years, P = 0.006, respectively. There was no association between SES area score and survival in an unadjusted model (hazard ratio [HR] 1.00, 95% confidence interval [CI] 0.99-1.01). Survival in the highest SES area quartile was superior to the lowest SES in a multivariable adjusted model including age, gender, and dialysis modality (HR 0.83, 95% CI 0.70-0.99, P = 0.04). These results were only mildly attenuated by censoring at time of transplantation (highest SES area quartile deprived vs. lowest SES area quartile, HR 0.85, 95% CI 0.70-1.03, P = 0.09). Superior patient survival was identified in the highest SES areas compared with the lowest following age-adjusted analyses, despite the older population in the most affluent areas. Further research should focus on identifying modifiable targets for intervention that account for this socioeconomic-related survival advantage.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".