P3‐069: MoCA contributions to differential diagnosis among normal controls, mild cognitive impairment and Alzheimer's disease in Brazil
Bibliographic record
Abstract
Diagnosis of cognitive decline, mainly Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) is still a challenge in developing countries, partly because of the limited data on cognitive screening instruments. The aim of this study was to describe the performance of the elderly assessed in a geriatric clinic, with five or more years of schooling, in the Montreal Cognitive Assessment (MoCA) and assess its association with other screening instruments. 53 patients, 60 years and older, with at least 5 years of education (66.04% had 8 years or more) were submitted to: Cambridge Cognitive Examination (CAMCOG); Mini-Mental State Examination (MMSE), Clock Drawing Test (CDT), MoCA Brazilian version, Geriatric Depression Scale (GDS) with 15 items, and Functional Activities Questionnaire (FAQ). The diagnostic criteria for dementia were based on the DSM-IV, NINDS-ADRDA criteria were used for AD, and for MCI we used Petersen et al. (2001). Fifteen participants were diagnosed with AD and 17 with MCI. Normal controls (NC) (21 participants) complained about memory problems but showed no evidence for dementia or MCI after neuropsychological assessment and neuroimaging. Indicated statistically significant differences (p < 0.001) among the three groups for the MoCA, MMSE, CAMCOG and PFAQ. Performance in the MoCA correlated strongly and significantly with MMSE (r=0.80, p < 0.001), CAMCOG (r=0.86, p < 0.001) and FAQ (r=0.80, p < 0.001). There was low but significant correlation between the MoCA and the CDT (Mendez scale r=0.39, p=0.003, Shulman scale r=0.43, p=0.001, Sunderland scale r=0.51, p < 0.001). There was no significant correlation with the GDS. The Brazilian version of the MoCA could differentiate the three diagnostic groups (AD, MCI and NC) and it correlated strongly with other instruments usually used for early detection of dementia in Brazil.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".