Évaluation de la compréhension orale dans la maladie de Parkinson : compréhension syntaxique et non littérale
Bibliographic record
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder whose motor symptoms are the best known. However, cognitive and language disorders are also present and require early speech therapy. Recent studies, although controversial, have shown the presence of a disorder of oral language comprehension, in particular syntactic and non-literal language comprehension, within the pathology. Given the lack of consensus on these results, we decided to investigate the presence of this receptive disorder. To this end, we analyzed the receptive language performance of 18 PD patients on the non-literal language comprehension tests of the Montreal Protocol for Communication’s Evaluation - Pocket version (MEC-P) and on the thematic role assignment task of the Syntactic Battery of Comprehension (BCS). These results were compared with those obtained in a control group of 17 gender and age matched subjects. The results show a significant deviation in the interpretation of metaphors, while inferential processes seem to be preserved (no statistically significant deviation for the realization of narrative discourse inference and speech act interpretation). The results also show no significant deviation in syntactic sentence comprehension for each type of sentence tested. Thus, no difficulties in syntactic comprehension were observed in our study, suggesting that syntactic interpretation skills are maintained in pathology. Our study suggests that there are difficulties with non-literal language comprehension in PD, particularly with the interpretation of metaphors. However, our study’s limits don't allow us to exclude other comprehension difficulties, including impairment of syntactic comprehension and inferential processes, present in previous research, inviting further investigations in this still poorly explored field.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".