Neurocognitive implications of diabetes on dementia as measured by an extensive neuropsychological battery.
Bibliographic record
Abstract
Diabetes is a disease with a deleterious pathology that currently impacts 4.5 million individuals within the United States. This study examined the ability of a specific neuropsychological battery to identify and classify dementia type, investigated the impact of diabetes on cognition and analyzed the ability of the memory measures of the 7 Minute Screen (7MS) and the Rey-Osterrieth Recall to correctly categorize dementia type when not used in combination with a full battery. The battery in addition to exhaustive patient history, medical chart review and pertinent tests were used in initial diagnosis. Results indicated the battery was sufficient in the identification and classification of dementia type. Within the sample, diabetes did not appear to significantly impact overall battery results whereby only two measures were minimally affected by diabetes. Finally, the memory measures of the 7MS and the Rey-Osterrieth Recall were sufficient to predict membership into the Alzheimer's (AD) and vascular dementia (VD) groups with 86.4% accuracy. The classification percentage dropped to 68.3% with addition of the mild cognitive impairment category. The full battery correctly classified AD and VD dementia 87.5% and appeared to be the most robust.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".