Cognitive impairment assessments in kidney transplantation: A review
Bibliographic record
Abstract
Mild cognitive impairment (CI) is not an absolute contraindication for kidney transplantation (KT). However, clinical assessment has not been standardized, and several practice challenges remain. We synthesized existing evidence on the effect of CI on adult kidney transplant recipients (KTRs) and KT candidates. Of the 1333 titles and abstracts screened, seven studies were eligible; all were observational. Our synthesis included 1035 KTRs and 4659 patients being evaluated for KT. Studies that used the Montreal Cognitive Assessment (38-55 %) reported a higher CI prevalence than those that used the Modified Mini-Mental State Exam (6-10 %). CI decreased the chances of KT waitlisting, however, the association with KT, graft loss and death varied by the cohort characteristics and tests used. The implications of our synthesis are limited by selection bias due to the exclusionary criterion, variability in tests and thresholds used. This may have misclassified participants with normal cognition as having CI and included those with dementia. Overall, additional evidence is needed to standardize the cognitive assessment of KTRs and candidates and inform clinical practice. A comprehensive assessment of cognition and function is indicated for the accurate diagnosis of CI, to determine CI severity, and to assess transplant candidacy.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".