Is There a Difference between Levodopa/ Dopa-Decarboxylase Inhibitor and Entacapone and Levodopa/Dopa-Decarboxylase Inhibitor Dose Fractionation Strategies in Parkinson’s Disease Patients Experiencing Symptom Re-Emergence due to Wearing-Off?
Bibliographic record
Abstract
Two strategies to manage symptom re-emergence due to wearing-off with conventional levodopa/dopa-decarboxylase inhibitor (DDCI) therapy were compared in patients with Parkinson's disease (PD) in this randomized, open-label trial. PD patients receiving 3 daily doses of levodopa/DDCI were randomized to either levodopa/DDCI and entacapone or an increased dose frequency of levodopa/DDCI with or without an increased total daily dose (dose fractionation). After 1 month of treatment, patients were followed up for 1 year. A greater proportion of levodopa/DDCI and entacapone-treated patients had treatment success compared with dose-fractionated patients, according to investigator Clinical Global Impression of Change scores at 1 month (68 vs. 59%, respectively) and 1 year (60 vs. 51%, respectively). Mean 'off' time (time with symptoms) was improved in both groups at 1 month and 1 year, despite a reduction in the mean daily levodopa dose in the levodopa/DDCI and entacapone group at 1 month. The mean daily levodopa dose was increased in the dose fractionation group. At 1 month, there was a 4% reduction in patients experiencing dyskinesia with levodopa/DDCI and entacapone and a 3% increase with dose fractionation. These data suggest that levodopa/DDCI and entacapone reduces time with symptoms, the rate of motor complications and the daily levodopa dose compared with dose fractionation. However, as the observed differences were not statistically significant, further studies are required to confirm these results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".