Impact of Predialysis Psychosocial Conditions on Kidney Transplant Recipient Survival: Evidence Using Propensity Score Matching
Bibliographic record
Abstract
Background: An increased number of end-stage renal disease patients suffer psychosocial conditions and may experience delayed access to transplantation due to listing restrictions. However, it remains to be shown whether preexisting psychosocial conditions confer an independent risk factor of poor posttransplant outcomes. Objective: We addressed this gap in knowledge by conducting a retrospective cohort study to investigate an independent association between the risk of death after transplant and having a diagnosis of psychosocial conditions 1 year prior to starting dialysis. Methods: All cases of adult deceased-donor kidney transplantation performed in Ontario, Canada, between April 1, 2002, and March 31, 2013, were used. Propensity score matching was applied to adjust for potential endogenous bias of using predialysis psychosocial status to predict posttransplant mortality. Survival analysis techniques, including Kaplan-Meier curves and Cox proportional hazards modeling, were also used. Results: Our results indicate a 49.4% (hazard ratio [HR] = 1.494 [95% confidence interval (CI) = 1.168-1.913]) increased relative risk of posttransplant death to be associated with predialysis psychosocial conditions, when other factors are held constant. The effect is significant ( P = .001) and is independent of other known predictors of death including advanced age. Conclusions: Findings from this study offered strong support for the development of psychosocial evaluation to screen candidates prior to transplant listing and early interventions for transplant candidates with psychosocial concerns.
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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 | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".