Social determinants of health and dialysis modality selection in patients with advanced chronic kidney disease: A retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Social determinants of health are non-medical factors that impact health. For patients with chronic kidney disease (CKD) progressing to kidney failure, the influence of social determinants of health on dialysis modality selection (haemodialysis vs. peritoneal dialysis (PD)) is incompletely understood. METHODS: Retrospective cohort study of 981 consecutive patients with advanced CKD referred to the Ottawa Hospital Multi-Care Kidney Clinic (Canada) who progressed to dialysis from 2010 to 2021. Multivariable logistic regression was used to measure odds ratios (OR) for the associations between social determinants of health (education, employment, marital status and residence) and modality of dialysis initiation. RESULTS: , respectively. Not having a high school degree was associated with lower odds of initiating dialysis via PD compared to having a college degree (29% vs. 48%, OR 0.55 (95% confidence interval (CI) 0.34-0.88)). Unemployment was associated with lower odds of initiating dialysis via PD compared to active employment (38% vs. 62%, OR 0.40 (95% CI 0.27-0.60)). Being single was associated with lower odds of initiating dialysis via PD compared to being married (35% vs. 48%, adjusted OR 0.52 (95% CI 0.39-0.70)). Living alone at home was associated with lower odds of initiating dialysis via PD compared to living at home with family (33% vs. 47%, adjusted OR 0.55 (95% CI 0.39-0.78)). CONCLUSIONS: Social determinants of health including education, employment, marital status and residence are associated with dialysis modality selection. Addressing these 'upstream' social factors may allow for more equitable outcomes during the transition from advanced CKD to kidney failure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".