Serotonin/dopamine transporter ratio as a predictor of <scp>l</scp> -dopa–induced dyskinesia
Bibliographic record
Abstract
l-3,4-Dihydroxyphenylalanine (l-dopa)-induced dyskinesia affects virtually every patient with Parkinson disease (PD) after chronic administration.1 The pathophysiology of dyskinesia is complex, and several nondopaminergic systems are involved.2 The serotonin (5-HT) system has received much attention over the past years as a critical player underlying dyskinesia and as a potentially efficacious therapeutic target. 5-HT neurons contain the required enzymes to synthesize dopamine from l-dopa3 but without the regulatory mechanisms required for dopaminergic transmission, resulting in dysregulated, nonphysiologic dopamine release, which underlies the dyskinetic state.4 However, if this aberrant dopamine release by 5-HT fibers is the culprit in dyskinesia, it is also responsible, at least partly, for the antiparkinsonian action of l-dopa.5 As a result, it has proven difficult to achieve an antidyskinetic benefit with agonists of the 5-HT1A receptors, which act by dampening this aberrant dopamine release by 5-HT terminals, without hindering the therapeutic effect of l-dopa in both preclinical and clinical settings.6,7
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".