Tele assessment of Communicative effectiveness among persons with Parkinson’s disease
Bibliographic record
Abstract
Purpose: Parkinson’s disease can exhibit neuro-communication disorders which can negatively impactthe quality of life. The current study aimed to investigate communicative effectiveness among individuals withmild and moderate Parkinson’s disease and matched controls via tele mode. Method: This cross-sectional study comprised of 20 Marathi-speaking individuals with Parkinson’s disease (mild = 10 & moderate = 10) and 20 matched controls who fulfilled the inclusion criteria. All participants underwent tele assessment comprising the Clinical Dementia Rating Scale, Montreal Cognitive Assessment in Marathi(MoCA-M), motor -speech subsection of MDS-Unified Parkinson's Disease Rating Scale(MDS-UPDRS), speech intelligibility subsection of FrenchayDysarthria Assessment-2(FDA-2) and Instrument to Assess Communicative Effectiveness(IACE). Results: Statistical analysis revealed a significant difference (p < 0.05) across individuals with Parkinson’s disease (mild, moderate) and control groups on the communicative domains (IACE).Teleassessment was helpful in identifying communicative morbidity and facilitate accessibility among PD patients who face disabiling physical challenges to avail SLP services. Across severity levels, performance in verbal comprehension and expression, reading, and writing was observed to be impacted. Conclusion: The communicative capabilities were seen to decline with an increase in severity of Parkinson’s disease. Both the mild and moderate Parkinson's disease groups demonstrated significant hypokinetic dysarthria and communicative impairments in various domains of IACE such as gestural communication, reading, writing, verbal comprehension, and expression, which had a direct impact on their overall communicative abilities. Tele assessment can be useful mode in rehabilitation of PD and improve communication related quality of life if timely identified and intervened.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".