A.4 Apomorphine effects on Parkinson’s disease fluctuation related pain
Bibliographic record
Abstract
Background: Fluctuation-related pain (worse in OFF periods) is a frequent and disabling symptom in Parkinson’s disease (PD). As evidence-based treatments to treat pain in PD are limited, exploring alternatives to treat it are imperative. Apomorphine is the only antiparkinsonian agent compatible with levodopa in improving PD motor symptoms and is usually well tolerated. We explored the effects of apomorphine in PD fluctuation-related pain. Methods: Small pilot double-blind, placebo controlled, randomized crossover study evaluating the safety and efficacy of subcutaneous apomorphine vs. placebo on fluctuation-related PD pain including participants experiencing pain during OFF periods. Primary outcomes: changes in a Visual Analogue Scale for pain and MDS-UPRDS III from baseline to 30 and 60 minutes after injections (two doses, separated by 60 min) and adverse events. Domperidone was used as premedication to avoid nausea/vomiting. Results: 16 patients were screened and 11 completed the study. All participants tolerated both treatments without significant side effects. Efficacy results remain blinded until the end of February 2023 and will be shown at the conference. Conclusions: Apomorphine, recently approved by Health Canada as an adjunctive therapy in PD patients and experiencing “off” periods, has shown to be safe when used to treat fluctuation-related PD pain. Efficacy outcomes will be soon available.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".