Associations of Pretransplant Patient-Reported Outcomes Measurement Information System Physical Function Score With Kidney Transplant Outcomes
Bibliographic record
Abstract
Simple and validated physical function measures are needed for kidney transplant candidates because pretransplant low physical function is a common and potentially modifiable risk factor. This single-center retrospective study investigated the associations between pretransplant physical function assessed by the Patient-Reported Outcomes Measurement Information System® Physical Function (PROMIS-PF) computer adaptive testing and early posttransplant outcomes. We analyzed 154 adult kidney-alone transplant recipients. The median pretransplant PROMIS-PF score was 43 (interquartile range, 39–47). Patient characteristics were not significantly different across the score category (normal, score ≥45; mild, score of 40–45; and moderate/severe, score <40). The PROMIS-PF score was not associated with length of transplant hospital stay, delayed graft function, 6-month and 12-month graft function, or 12-month patient and graft survival. However, a lower PROMIS-PF score was significantly associated with a higher risk of emergency room visits [adjusted odds ratios compared to normal: mild, 1.68 (95% confidence interval, 0.76–3.83); moderate/severe, 3.23 (1.34–7.79)] and rehospitalization [adjusted odds ratios: mild, 2.61 (1.16–5.90); moderate/severe, 2.53 (1.07–6.00)] within 1 month posttransplant. Results suggest that PROMIS-PF is a practical tool for assessing physical function in kidney transplant candidates. Larger studies are needed to confirm the utility of PROMIS-PF to identify transplant candidates who would benefit from pretransplant prehabilitation.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".