Montreal Cognitive Assessment for cognitive assessment in chronic kidney disease: a systematic review
Bibliographic record
Abstract
INTRODUCTION: There is evidence in the literature that cognitive impairment is more prevalent in individuals with chronic kidney disease (CKD) than in the general population. The Montreal Cognitive Assessment (MoCA) is an instrument with a good application profile for cognitive evaluation of patients with CKD-like impairments. The objective of this study is to perform a systematic review of MoCA use in the context of CKD. METHOD: The keywords "Montreal Cognitive Assessment", "Kidney Disease" and "Chronic Kidney Disease" were used to search the databases. The inclusion criteria were: a) empirical articles; b) approach to cognitive impairment in CKD; c) papers in Portuguese and English. RESULTS: The studies were mostly cross-sectional, published in medical journals, with research carried out mostly in Europe. About 45% of the studies had samples of less than 150 participants and variations in the prevalence of cognitive impairment were found ranging from 28.9% to 74.6%. The cutoff point for the identification of the impairment presented variation between the studies. DISCUSSION: The results' analysis demonstrates the need for more complete studies on MoCA scoring and adaptation in its different versions. We recommend to the health professionals who will use the results in the clinical setting that the interpretation of the results be made in the light of studies more related to the context lived by the patients. CONCLUSIONS: The instrument is efficient to be used in several stages and treatment modalities of the disease. We point to the need to adapt a cut-off point for the instrument in the different translations of the instrument.
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 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 | 0.011 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".