Differential effects of a multidomain intervention on cognitive decline in older adults with type 2 diabetes according to white matter hyperintensity status: A secondary analysis of the J‐ <scp>MIND</scp> ‐Diabetes
Bibliographic record
Abstract
AIMS: White matter hyperintensities (WMHs) are commonly observed in older adults with type 2 diabetes. The current study aimed to investigate whether WMH modifies the effects of multidomain intervention in preventing cognitive decline among older adults with type 2 diabetes and mild cognitive impairment. MATERIALS AND METHODS: This secondary analysis of the Japan-Multidomain Intervention Trial for Prevention of Dementia in Older Adults with Diabetes included 154 participants aged 70-85 years who presented with type 2 diabetes and mild cognitive impairment. They were randomized into the intervention (vascular risk management, exercise, nutritional counselling and promotion of social activities) and control (provision of health-related information) groups. The primary outcome was a change in average Z-scores from all of the neuropsychological tests combined, and secondary outcomes were domain-specific composite scores (memory, executive function and processing speed) from baseline to 18 months. The presence of WMH was assessed using the Fazekas scale. The associations between the intervention and baseline WMH were evaluated using a mixed-effects model for repeated measures. RESULTS: Among 90 participants included in the analyses, 34 had moderate to severe WMH. At the 18-month follow-up, a significant intervention-WMH interaction (p = 0.017) was found for the primary outcome. The intervention effect was significant in individuals with WMH (Z-score difference: +0.335, 95% confidence interval [CI]: +0.045 to +0.624), but not in individuals without WMH (Z-score difference: -0.121, 95% CI: -0.353 to +0.110). CONCLUSIONS: Older adults with type 2 diabetes and WMH may benefit from multidomain interventions. Further studies should be performed to validate this finding.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".