Bibliographic record
Abstract
BACKGROUND: Dementia diagnosis and the various subtypes are challenging in the absence of biomarkers. AIM: To examine available tests and neuroimaging procedures that may help distinguish these disorders. METHODS: Alzheimer's disease (AD), cognitive vascular disorder (CVD), and Frontotemporal lobe disorders (FTLD) were tested with a hierarchical neuropsychological battery that included the Frontal Systems Behavior Scale, Mini-Mental State Examination, Montreal Cognitive Assessment Test, and subtests. All patients had multimodality magnetic resonance imaging and (18)F fluorodeoxyglucose-positron emission tomography (FDG-PET) brain scans. RESULTS: Of the 161 patients evaluated for dysmemory and cognitive impairment, 31 satisfied the full protocol. The mean T scores for the 3 principal frontal system syndromes for the AD group were all abnormal save disinhibition. For the CVD and FTLD groups, all the 4 subcategory scores were abnormal. Disinhibition differed significantly between the AD and FTD group (analysis of variance [ANOVA], P = .02) and there was a strong association between the memory for 5 words test and a significant difference in the word list generation test score among the 3 groups (ANOVA, P = .0233). There was a strong association between the FDG-PET and the disease subtype (P < .0001). CONCLUSION: Evaluation for disinhibition, word list generation, 5-word memory testing and PET brain imaging may help distinguish the 3 most common dementia subtypes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".