Connected speech profiles in mild cognitive impairment reflect global cognition
Bibliographic record
Abstract
BackgroundMild cognitive impairment (MCI), a prodromal stage of Alzheimer's disease (AD) for many individuals, is accompanied by widespread connected speech (CS) changes (e.g., shorter CS samples, mention of fewer semantic content units, lower syntactic complexity). Nevertheless, findings on CS in MCI are heterogeneous. This heterogeneity, combined with the heterogeneity in cognition in MCI suggests that there could exist more than one CS profile in this population.ObjectiveWe aimed to determine if there are multiple CS profiles in MCI and whether these potential CS profiles are characterized by distinct cognitive presentations.MethodsCS characteristics were extracted from the samples of 109 controls and 210 individuals with MCI from the COMPASS-ND study database. A Two-Step Cluster Analysis was then carried out to identify potential CS profiles in MCI. These profiles were compared to one another and to controls in terms of their linguistic and cognitive characteristics.ResultsWe identified two CS profiles in MCI, characterized by reduced syntactic complexity and semantic content and by dysfluencies, longer CS samples, and reduced semantic idea density and efficiency, respectively. The reduced semantic content/syntactic complexity profile was also characterized by various cognitive difficulties (e.g., visuospatial, episodic memory, executive functioning domains) in comparison with controls, whereas the increased production and reduced idea transmission effectiveness profile had relatively isolated episodic memory difficulties.ConclusionsCS analysis could be a helpful screening tool to identify individuals with MCI who show greater cognitive difficulties and who would most benefit from more extensive cognitive and/or medical testing as well as from cognitive and/or psychological interventions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".