Bibliographic record
Abstract
Susan Fox opened this satellite symposium at the 8th European Academy of Neurology (EAN) Congress with an overview of the concept of motor fluctuations (MF) in Parkinson’s disease (PD). She emphasised that levodopa remains the gold standard therapy for PD. However, MFs are one of the critical complications of levodopa therapy that affect many patients with advancing PD and, when diagnosed, represent a challenge in patient management. Alternative options are, therefore, needed to provide continuous dopaminergic stimulation while maximising the levodopa benefit. Despite different options, Angelo Antonini showed that neurologists often prefer to adjust levodopa dose rather than add an adjunctive agent. Market research confirms that, in patients with PD, the levodopa dose is adjusted in around 80% of patients, while only 20% have adjunct therapy as a first-line option. Adjusting the levodopa dose, either by increasing or fractionating the dose, or both, remains a valid, tried-and-tested option, although it has limitations. Joaquim Ferreira presented emerging evidence from a Phase II clinical trial, suggesting a potential benefit of adding opicapone 50 mg compared with 100 mg levodopa to treat patients with PD and end-of-dose fluctuations. This symposium aimed to present the effect of opicapone with relatively low total daily doses of levodopa; an option that may not have been traditionally considered by neurologists who are used to adjusting levodopa as a first-line response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".