Task specific focal hand dystonia: Understanding the enigma and current concepts
Bibliographic record
Abstract
OBJECTIVE: The first step in approaching task specific focal hand dystonia (TSFHD) is recognition that it is a neurological disorder and not a deficiency in practice or technique. To eliminate the enigma, TSFHD needs to be a more familiar entity. That is the objective of this paper. METHOD: This is a state of the art review in concert with 3 decades of experience providing care for musicians written to act as a reference source. It is written as an introduction to TSFHD by reviewing history, etiology and current theories, presentation and characteristics, diagnosis and treatment. CONCLUSIONS: Information sources, both web-based and by consultation need to be accessible, reliable and comprehensive. Accurate diagnosis should include the diagnosis of concurrent impairments and the confirmation that the diagnosis of TSFHD is correct. Successful treatment is likely to be interdisciplinary. Successful approaches may include the administration of botulinum toxin but approaches should not be restricted to pharmaceuticals. Instrument modification, altering technique and sensory motor retraining are potential adjunctive approaches. A dichotomy exists between the therapeutic benefit achieved with treatment and the musician's need for optimum hand function. The final goal is successful return to playing at a level that meets the musician's needs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".