History of psychosis and mania, and outcomes after kidney transplantation - a retrospective study
Bibliographic record
Abstract
History of psychosis or mania, if uncontrolled, both represent relative contraindications for kidney transplantation. We examined 3680 US veterans who underwent kidney transplantation. The diagnosis of history of psychosis/mania was based on a validated algorithm. Measured confounders were used to create a propensity score-matched cohort (n = 442). Associations between pretransplantation psychosis/mania and death with functioning graft, all-cause death, graft loss, and rejection were examined in survival models and logistic regression models. Post-transplant medication nonadherence was assessed using proportion of days covered (PDC) for tacrolimus and mycophenolic acid in both groups. The mean ± SD age of the cohort at baseline was 61 ± 11 years, 92% were male, and 66% and 27% of patients were white and African-American, respectively. Compared to patients without history of psychosis/mania, patients with a history of psychosis/mania had similar risk of death with functioning graft [subhazard ratio (SHR) (95% confidence interval (CI)): 0.94(0.42-2.09)], all-cause death [hazard ratio (95% CI): 1.04 (0.51-2.14)], graft loss [SHR (95% CI): 1.07 (0.45-2.57)], and rejection [odds ratio(95% CI): 1.23(0.60-2.53)]. Moreover, there was no difference in immunosuppressive drug PDC in patients with and without history of psychosis/mania (PDC: 76 ± 21% vs. 78 ± 19%, P = 0.529 for tacrolimus; PDC: 78 ± 17% vs. 79 ± 18%, P = 0.666 for mycophenolic acid). After careful selection, pretransplantation psychosis/mania is not associated with adverse outcomes in kidney transplant recipients.
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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 | 0.000 |
| 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 | 0.000 | 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".