Revisiting the Montreal Cognitive Assessment in a European cohort of elderly living with type 2 diabetes
Bibliographic record
Abstract
BackgroundIndividuals with type 2 diabetes have an increased risk of developing both vascular and Alzheimer's dementia.ObjectiveThis prospective cross-sectional study assessed the screening ability of the standard Montreal Cognitive Assessment (MoCA) score suggestive of mild cognitive impairment (<26) in a European cohort of individuals ≥65 of age with type 2 diabetes.MethodsParticipants of RECOGNISED, a European prospective EU-funded cohort study, were screened using MoCA. In addition, a 13-item Neuropsychological Test Battery (NTB) with the Clinical Dementia Rating was undertaken to categorize participants as normocognitive (NC, n = 128) or mild cognitive impaired (MCI, n = 185). Receiver operating characteristic (ROC) analysis was used to evaluate the ability of MoCA cut-off scores to categorize patients as having MCI or not.ResultsThe standard MoCA cut-off of 25/26 demonstrated a sensitivity of 88% and a specificity of 51%, resulting in a false positive rate of 20%. ROC analysis showed that a MoCA cut-off of 24/25 has a better balance between sensitivity (81%) and specificity (62%), with a lower false positive rate of 16%. NTB results showed that the MCI group had the lowest norm-referenced percentile scores in the visuo-construction domain, a known early feature of Alzheimer's disease and a significant predictor of a rapid rate of disease progression.ConclusionsMoCA as a screening tool in individuals ≥65 with type 2 diabetes, overestimates the prevalence of MCI, even when applying lower cut-offs. More specific screening strategies are necessary, particularly targeting the visuo-construction domain, to effectively identify cognitive impairment in individuals with type 2 diabetes.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".