Placebo and nocebo responses in other movement disorders besides Parkinson's disease: How much do we know?
Bibliographic record
Abstract
Among movement disorders and medicine in general, PD is one of the conditions for which there is a greater knowledge of the placebo and nocebo responses. In other movement disorders, the knowledge of placebo and nocebo responses is less. An advance in this field is expected to contribute to a better understanding of the nature of a therapeutic benefit in clinical research and clinical practice, and mechanisms of placebo and nocebo. We conducted a review on placebo and nocebo responses in other movement disorders besides PD by primarily examining meta-analyses of clinical trials assessing specifically the placebo and/or nocebo responses. Second, we examined both efficacy and safety results of a placebo arm in pivotal placebo-controlled trials for the different movement disorders. RLS is the movement disorder for which the most data exist, followed by tic disorders and Huntington's disease. Data available in other conditions document a placebo response in a varied phenomenology. We found different placebo responses according to clinical domains assessed, type of outcomes used for the same clinical domain, and modes of treatment administration, including rehabilitation and surgical interventions. Data on the nocebo response were very scarce. Although the data available are limited, RLS has the better documentation of placebo and nocebo responses. In contrast, atypical parkinsonisms are the group of movement disorders with the least knowledge. The clinical entities with a more robust placebo response are those that have the most beneficial available symptomatic treatments. © 2018 International Parkinson and Movement Disorder Society.
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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 | 0.047 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".