Patient-Reported Markers of Delayed Physical Function Recovery After Kidney Transplantation: Supervised Cluster Analysis of PROMIS Physical Function Scores
Bibliographic record
Abstract
Background: Kidney transplant recipients (KTRs) with delayed physical function (PF) recovery post-transplant may benefit from early rehabilitation support. We aimed to identify patterns of PF recovery using supervised cluster analysis of longitudinally obtained Patient Reported Outcome Measurement System (PROMIS) PF scores. Methods: Longitudinal convenience sample of adult KTRs who completed PROMIS-PF Computer Adaptive Test (CAT) (higher=better PF) within ~1 week post-transplant and biweekly over 2 months. We stratified participants by baseline PROMIS-PF T-score (>30 vs. ≤30) and by T-score change between baseline and week 2 (≥2 vs <2; improved vs. non-responder). Four clusters (EXPOSURE) were identified (Fig. 1): CL1(poor baseline– non-responder), CL2(poor baseline–improved), CL3(better baseline–non-responder), CL4 (better baseline–improved). We used linear mixed-effects models with participant random effects and time-by-cluster interactions to assess trajectories, and interval regression to estimate time-to-recovery (T-score ≥45-OUTCOME), followed by Wald tests for group comparisons. Results: Of 63 participants, 3(5%) were in CL1, 15(24%) in CL2, 37(59%) in CL3, and 8(12%) in CL4. Age, organ type, socioeconomic status, and ethnicity were similar across clusters. CL1 were all females, and had highest (most fatigue) baseline PROMIS fatigue scores; other baseline patient-reported outcomes characteristics were similar across clusters. At 2 months post-transplant, mean(95% CI) PROMIS-PF scores were: CL1: 32(26–38) vs CL2: 43(40–46)(p=0.001); CL3: 46(44–48) vs CL4: 51(47–55)(p=0.01). The proportion who reached outcome event were 0%, 54%, 54%, and 94% in CL1,CL2,CL3 andCL4, respectively(p=0.01). Conclusion: Baseline and early PROMIS-PF change identified four distinct recovery clusters among KTRs. Funding: Government Support – Non-U.S.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".