Drug-induced dyskinesia in Parkinson's disease. Should success in clinical management be a function of improvement of motor repertoire rather than amplitude of dyskinesia?
Bibliographic record
Abstract
BACKGROUND: Dyskinesia, a major complication in the treatment of Parkinson's disease (PD), can require prolonged monitoring and complex medical management. DISCUSSION: The current paper proposes a new way to view the management of dyskinesia in an integrated fashion. We suggest that dyskinesia be considered as a factor in a signal-to-noise ratio (SNR) equation where the signal is the voluntary movement and the noise is PD symptomatology, including dyskinesia. The goal of clinicians should be to ensure a high SNR in order to maintain or enhance the motor repertoire of patients. To understand why such an approach would be beneficial, we first review mechanisms of dyskinesia, as well as their impact on the quality of life of patients and on the health-care system. Theoretical and practical bases for the SNR approach are then discussed. SUMMARY: Clinicians should not only consider the level of motor symptomatology when assessing the efficacy of their treatment strategy, but also breadth of the motor repertoire available to patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".