Social determinants of health and the transition from advanced chronic kidney disease to kidney failure
Bibliographic record
Abstract
BACKGROUND: The transition from chronic kidney disease (CKD) to kidney failure is a vulnerable time for patients, with suboptimal transitions associated with increased morbidity and mortality. Whether social determinants of health are associated with suboptimal transitions is not well understood. METHODS: This retrospective cohort study included 1070 patients with advanced CKD who were referred to the Ottawa Hospital Multi-Care Kidney Clinic and developed kidney failure (dialysis or kidney transplantation) between 2010 and 2021. Social determinant information, including education level, employment status and marital status, was collected under routine clinic protocol. Outcomes surrounding suboptimal transition included inpatient (versus outpatient) dialysis starts, pre-emptive (versus delayed) access creation and pre-emptive kidney transplantation. We examined the association between social determinants of health and suboptimal transition outcomes using multivariable logistic regression. RESULTS: The mean age and estimated glomerular filtration rate were 63 years and 18 ml/min/1.73 m2, respectively. Not having a high school degree was associated with higher odds for an inpatient dialysis start compared with having a college degree {odds ratio [OR] 1.71 [95% confidence interval (CI) 1.09-2.69]}. Unemployment was associated with higher odds for an inpatient dialysis start [OR 1.85 (95% CI 1.18-2.92)], lower odds for pre-emptive access creation [OR 0.53 (95% CI 0.34-0.82)] and lower odds for pre-emptive kidney transplantation [OR 0.48 (95% CI 0.24-0.96)] compared with active employment. Being single was associated with higher odds for an inpatient dialysis start [OR 1.44 (95% CI 1.07-1.93)] and lower odds for pre-emptive access creation [OR 0.67 (95% CI 0.50-0.89)] compared with being married. CONCLUSIONS: Social determinants of health, including education, employment and marital status, are associated with suboptimal transitions from 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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".