Naturalistic evaluation of entacapone in patients with signs and symptoms of<scp>L</scp>-dopa wearing-off
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of entacapone in the management of levodopa wearing-off in Parkinson's disease (PD) in a naturalistic, real-life setting. RESEARCH DESIGN AND METHODS: This prospective, open-label, observational study included patients with idiopathic PD. Patients were eligible for inclusion if they had been taking 3-5 doses of levodopa per day for ≥2 months and had shown signs of levodopa wearing-off for ≥1 month. Subjects received entacapone (recommended dose: 1 × 200 mg tablet with each levodopa dose) for 28 days. Patients were asked to complete a wearing-off questionnaire and the eight-question Parkinson's Disease Questionnaire Quality of Life assessment (PDQ-8). Activities of daily living (both in the on and off states) were assessed using the Unified Parkinson's Disease Rating Scale (UPDRS) part II. Clinical Global Impression (CGI) of severity of PD-related symptoms was assessed using a modified CGI tool. Patient global assessment of severity of PD symptoms was also obtained. RESULTS: A total of 341 patients were enrolled by 68 physicians across Canada. At Day 28, 56.9% of the subjects indicated improvement compared to baseline on the modified CGI of change (CGI-C); 21.4% reported no change. Improvements were also observed on the UPDRS II and the PDQ-8. Benefit from entacapone appeared to be relatively uniform across subgroups (e.g., number of daily levodopa doses, use of other anti-PD medications). STUDY LIMITATIONS: The results of this study may be biased due to factors inherent in open-label, community-based trials (e.g., compliance). This is, however, reflective of everyday clinical practice. CONCLUSIONS: In this naturalistic, real-life study, the addition of entacapone to levodopa therapy provided benefits in quality of life and activities of daily living for a substantial proportion of PD patients experiencing wearing-off.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".