The influence of disease severity on verbal fluency performance in Parkinson's disease
Bibliographic record
Abstract
Verbal fluency is often included as part of cognitive assessment in Parkinson's disease (PD). It has been related not only to executive processes, but also to semantic processing ability. Thus, verbal fluency represents an important domain for monitoring cognitive decline in cortical and subcortical disorders. Although differences in verbal fluency have been reported when comparing PD and controls, little attention has been devoted to understanding whether motor symptom severity might be associated with verbal fluency performance. The aim of the present study was to investigate the relationship between motor symptom severity and phonetic and category verbal fluency. Thirty-one PD patients with lower (n=15) and higher (n=16) UPDRS motor scores were assessed in phonetic (letters) and category (animal) verbal fluency. The results show that the more impaired group was able to generate fewer words in both tests, indicating that the progression of disease severity in PD has an impact on verbal fluency, especially in phonetic fluency. Looking more closely at performance revealed that the more impaired patients exhibited reduced switching in the phonemic fluency task which has been partly attributed to slower movement initiation. In the category fluency task the more impaired patients had more difficulty with clustering words which could be related to slowness in thinking (bradyphrenia) observed in PD patients in the later stages of the disease.
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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 | 0.001 | 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".