Viewpoint: Developing drugs for levodopa‐induced dyskinesia in <scp>PD</scp>: Lessons learnt, what does the future hold?
Bibliographic record
Abstract
The drive to develop drugs to treat PD starts and ends with the patient. Herein, we discuss how the experience with drug development for LID has led the field in translational studies in PD with advancing ground-breaking science via rigorous clinical trial design, to deliver clinical proof-of-concepts across multiple therapeutic targets. However, issues remain in advancing drugs efficacious preclinically to the clinic, and future studies need to learn from past successes and failures. Such lessons include implementing better early indicators of tolerability, for instance evaluating non-motor symptoms in preclinical models; improving patient-related outcome measures in clinical trials, as well as considering the unique nature of dyskinesia in an individual patient. The field of translational studies needs to become more patient focused to improve successful outcomes.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".