Can the Timed Up & Go Test and Montreal Cognitive Assessment predict outcomes in patients waitlisted for renal transplant?
Bibliographic record
Abstract
PURPOSE: Frail patients who undergo renal transplantation (RT) have more complications; however, little is known if these patients can sustain the wait to RT. We used the Timed Up and Go Test (TUGT) and Montreal Cognitive Assessment (MoCA) to determine outcomes of RT candidates. METHODS: In this retrospective study, 526 RT candidates underwent TUGT and MoCA (2015-2019) and were divided into "favorable" (transplanted or remained on the list) or "unfavorable" (not listed, removed from list, or died) outcome. Demographics, education, language, comorbidities, dialysis type, use of a walking device, TUGT, and MoCA were compared by outcome. RESULTS: Overall, 230 patients (43.7%) passed TUG, 268 (51%) passed MoCA, 133 (25.3%) passed both, and 161 (30.6%) failed both tests. Multivariate analysis demonstrated age ≥ 65 (OR 1.58, CI 1.03-2.43), cardiac disease (OR 3.09, CI 2.02-4.72), ≥36 months on dialysis (OR 1.80, CI 1.24-2.69), EPTS < 20% at time of MoCA (OR 0.26, CI 0.07-0.98), and failing TUGT (OR 2.14, CI 1.43-3.19) were associated with unfavorable outcome. Failing MoCA was not associated with outcome. CONCLUSIONS: MoCA test results were not associated with RT waitlist outcomes; however, passing the TUGT was associated with receiving RT or remaining on the list. Additional studies are needed to validate this and determine outcome after RT.
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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 | 0.004 |
| 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".