Evaluation of Linguistic Markers of Word-Finding Difficulty and Cognition in Parkinson's Disease
Bibliographic record
Abstract
Purpose: Early cognitive symptoms such as word-finding difficulty (WFD) in daily conversation are common in Parkinson's disease (PD), but studies have been limited by a lack of feasible, quantitative measures. Linguistic analysis, focused on pauses in speech, may yield markers of impairment of cognition and communication in PD. The objective of this study was to evaluate the relationship of linguistic markers in semistructured speech to WFD symptoms and cognitive function in PD. Method: Speech recordings of description of the Cookie Theft picture in 53 patients with PD without dementia and 23 elderly controls were analyzed with Praat software. Montreal Cognitive Assessment (MoCA; Nasreddine et al., 2005), category naming fluency, and confrontation naming tests were administered. Questionnaires rating WFD symptoms and cognitive instrumental activities of daily living were completed. We determined the relationships between (a) pause length and location, (b) MoCA score, and (c) WFD symptoms, using Pearson's correlations and multivariate regression models. Results: Compared with controls, patients with PD had more pauses within utterances as well as fewer words per minute and a lower percentage of well-formed sentences. Pauses within utterances differed significantly between PD-mild cognitive impairment and normal cognition (p < .001). Words per minute and percentage of well-formed sentences were predictive of MoCA in multivariate regression models. Pauses before verbs were associated with patient-reported severity of WFD symptoms (p = .006). Conclusions: Linguistic markers including pauses within utterances distinguish patients with PD with mild cognitive symptoms from elderly controls. These markers are associated with global cognitive function before the onset of dementia. Pauses before verbs and grammatical markers may index early cognitive symptoms such as WFD that may interfere with functional communication. Supplemental Material: https://doi.org/10.23641/asha.6615401.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".