Comparison of Behavior-Related Features in the MMSE Sentence in Behavioral Variant Frontotemporal Dementia and Alzheimer’s Disease
Bibliographic record
Abstract
Background: Despite the ubiquity of cognitive assessments using the MMSE, there has been little investigation of currently unscored features of the MMSE sentence item relevant to behavior and language in patients with behavioral variant Frontotemporal Dementia (bvFTD) and Alzheimer’s disease (AD). Objective: To describe and compare the unscored content and grammar elements of the MMSE sentence item in patients with bvFTD and AD. Methods: Categorization of predefined content and grammar elements of the MMSE sentence was performed by two blinded raters in patients with bvFTD (n = 74) and AD (n = 84). Chi-square and ANCOVAs were conducted to identify differences between the diagnostic groups. A multinomial logistic regression analysis was conducted to determine whether these features aid in the prediction of diagnosis of bvFTD or AD. Results: A higher proportion of patients with bvFTD wrote sentences addressed to the examiner (22.7% vs. 4.7%, X2 = 11.272, p = 0.001) and about interpersonal relationships (35.3% vs. 16.0%, X2 = 10.139, p = 0.017) in comparison to those with AD. The number of words written was lower in patients with AD and was positively correlated with lower total MMSE scores in AD but not in bvFTD (AD: r = 0.370, p < 0.001; FTD: r = 0.209, p = 0.07). Assessment of the MMSE sentence content and grammar variables did not add to the prediction bvFTD or AD diagnosis beyond the variance explained by age and total MoCA score. Conclusions: Patients with bvFTD and AD showed differences in aspects of the content of the written MMSE sentence item, though these differences did not aid in the diagnosis prediction.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".