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Record W4411920898 · doi:10.1016/j.trre.2025.100940

Cognitive impairment assessments in kidney transplantation: A review

2025· review· en· W4411920898 on OpenAlexafffundabout
Safaa Azzouz, Donald Doell, Marcelo Cantarovich, Kathleen Gaudio, Shaifali Sandal

Bibliographic record

VenueTransplantation Reviews · 2025
Typereview
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsMcGill University Health CentreMcGill University
FundersFonds de Recherche du Québec - SantéAmgen CanadaAstraZeneca
KeywordsMedicineCognitive impairmentKidney transplantationIntensive care medicineTransplantationCognitionInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.390
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.079
GPT teacher head0.445
Teacher spread0.366 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2025
Admission routes3
Has abstractyes

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