P3–224: Effectiveness of the Montreal Cognitive Assessment (MoCA) for assessing frontal hypoperfusion in people with vascular dementia: The Osaki‐Tajiri Project
Bibliographic record
Abstract
The previous study showed that the Montréal Cognitive Assessment (MoCA) was effective for evaluate cerebrovascular diseases. We also showed the test was effective for screening very mild stage of VaD in the community. We herein examined the effectiveness of MoCA at the out-patients clinic to assess patients with vascular dementia (VaD). 44 patients with VaD (NINDS-AIREN) and 58 patients with Alzheimer's disease (AD) (NINCDS-ADRDA) were compared with 67 non-demented subjects. All were outpatients at the Tajiri Memory Clinic, Osaki-Tajiri, northern Japan. All received 1.5T-MRI as well as ECD-SPECT examinations. The SPECT images were used to classify the VaD patients into two subgroups, i.e., those with frontal hypoperfusion (F-VaD) and those without frontal hypoperfusion (NF-VaD). The frontal hypoperfusion pattern was defined as the “P2” pattern of the Sliverman classification with or without focal hypometabolism in other areas (JAMA 2001;286:2120) based on three neurologists' agreement blindly to the neuropsychological examinations. All subscale scores on the MoCA were found to be lower in the VaD as well as AD groups compared with the normal controls, with no difference between the two dementia groups. The Attention subscale score was lower in the F-VaD subgroup compared with those in the NF-VaD subgroup (p<0.01, corrected for multiple comparison). Our results suggested that the MoCA attention subscale can detect the characteristics of VaD participants, especially those with frontal hypoperfusion.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".