Voxel‐based specific regional analysis system for Alzheimer’s disease utility as a screening tool for unrecognized cognitive dysfunction of elderly patients in diabetes outpatient clinics: Multicenter retrospective exploratory study
Bibliographic record
Abstract
AIMS/INTRODUCTION: An efficient screening strategy for identification of cognitive dysfunction remains a clinical issue in the management of elderly adults with diabetes. A magnetic resonance imaging voxel-based specific regional analysis system for Alzheimer's disease (VSRAD) has been developed as an automated brain morphometry system that includes the hippocampus. We carried out a multicenter retrospective study to evaluate the utility of VSRAD for screening cognitive dysfunction in diabetes outpatient clinics. MATERIALS AND METHODS: We enrolled patients with diabetes aged >65 years who underwent brain magnetic resonance imaging scans for the purpose of a medical checkup between November 2018 and May 2019. Patients who were already suspected or diagnosed with mild cognitive impairment and/or dementia as well as those with a history of cerebrovascular disease were excluded. RESULTS: A total of 67 patients were enrolled. Five patients were diagnosed with mild cognitive impairment or dementia (clinical cognitive dysfunction). Patients with clinical cognitive dysfunction showed a significantly higher z-score in VSRAD analysis (2.57 ± 0.47 vs 1.15 ± 0.55, P < 0.01). The sensitivities and specificities for diagnosis of clinical cognitive dysfunction were 80 and 48% for the Mini-Mental State Examination, 100 and 89% for the z-score, and 100 and 90% for the combination of the Mini-Mental State Examination score and z-score, respectively. CONCLUSIONS: VSRAD analysis can distinguish patients with clinical cognitive dysfunction in the elderly with diabetes, and also shows reasonable sensitivity and specificity compared with the Mini-Mental State Examination alone. Thus, VSRAD analysis can be useful for early identification of clinical cognitive dysfunction in the elderly with diabetes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".