Use of the Japanese Version of the Montreal Cognitive Assessment to Estimate Cognitive Decline in Patients Aged 75 Years or Older with and without Type 2 Diabetes Mellitus
Bibliographic record
Abstract
BACKGROUND: The number of people diagnosed with dementia worldwide is set to increase significantly. Patients with dementia often have comorbidities, particularly diabetes, and patients with type 2 diabetes mellitus (T2DM) have a high risk of cognitive decline. This study investigated whether older people with T2DM have disease-specific cognitive deficits. METHODS: The Montreal Cognitive Assessment is a well-known tool for examining mild cognitive impairment, and the modified Japanese version (MoCA-J) has been confirmed as effective. Using the MoCA-J, we assessed the cognitive function of Japanese adults aged ≥75 years with and without T2DM and analyzed the results. RESULTS: Thirty-three patients with T2DM and 23 non-DM patients completed the examination, and MoCA-J total scores differed between these groups (T2DM mean, 21.4 ± 3.5; non-DM mean, 23.5 ± 3.6). Only 9% of patients with T2DM and 39% of those with non-DM had scores ≥26, which is the cutoff point for mild cognitive impairment, although all patients were capable of self-care. Additionally, delayed recall scores were significantly lower for the older patients with T2DM had for the non-DM group. CONCLUSIONS: Patients aged ≥75 years with T2DM might have worse cognition than those without T2DM; the inability to perform delayed recall in T2DM patients suggests a decline in cognitive function. Therefore, patients aged ≥75 years with T2DM should receive explanations of their care that are individualized in relation to their cognitive status.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".