L’impact des facteurs psychologiques dans les dystonies du musicien : revue systématique
Bibliographic record
Abstract
Background: Musician’s dystonia is a focal dystonia characterize by involuntary contraction that reduces motor control while playing an instrument. Professional musicians are exposed to different psychological factors while performing, such as stress, anxiety or perfectionnism.Objective: The aim of this study is to know how psychological factors can have an influence on the musician’s dystonia.Methods: 5 studies were included in the review. From the different digital databases (PubMed, Pedro, Cochrane, ScienceDirect). Inclusion and exclusion criteria were defined to guide the selection. The methodological quality of the 5 studies included was assessed with the Newcastle Ottawa Scale.. The research was conducted from September 2024 to April 2025.Results: Statistical analyses reveal a correlation between certain sub-scores of the SCQ or FMPS and the presence of musician’s dystonia. However, the data do not support a significant influence of anxiety as assessed by the CTAI, STAI and NEO-FFI on the musician’s dystonia.Discussion: The psychological management of a musician suffering from dystonia should not be overlooked during consultation. Further studies are recommended to determine whether these psychological factors are a cause or a consequence of the disorder, and to explore additional elements such as adverse childhood experiences.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".