Micro-linguistic measures analysis of connected speech in mild cognitive impairment
Bibliographic record
Abstract
Background As language deficits have been found to be a strong predictor of conversion from MCI (Mild Cognitive Impairment) to dementia, connected speech analysis provides sensitive measures of cognitive decline through micro-linguistic features.Aims This study investigated specific linguistic measures in connected speech of MCI patients and healthy controls (HC), examining differences in micro-linguistic features and their correlation with cognitive function.Methods & procedures We analyzed language samples from 40 MCI patients and 22 healthy controls from the Delaware English Protocol Corpus of Dementia Bank. Participants completed five language tasks including picture descriptions, story retelling, and procedural narratives. An independent t-test was performed to compare the linguistic measures between the MCI group and the HC group. Correlation analysis showed highly positive relationships were excluded and the remaining variables were then used as predictors in the regression analysis. Stepwise multiple linear regression analysis to examine the influence of linguistic feature on cognitive function (measured by Montreal Cognitive Assessment scores).Outcomes & results MCI patients demonstrated significantly reduced lexical semantic measures and morpho-syntactic measures. Correlation analysis showed highly positive relationships between MLU in morphemes and Verb Utt. Stepwise multiple linear regression identified MLU in morphemes as a significant predictor of cognitive status (B = 0.79, F (1, 60) = 16.26, p < 0.01).Conclusion MCI patients exhibit distinct patterns of linguistic impairment characterized by reduced lexical diversity, simplified syntactic structures, and decreased propositional density. MLU in morphemes emerges as a particularly valuable linguistic marker for cognitive assessment and may sensitively reflect cognitive variation, which have auxiliary value to distinguish and predict MCI.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".