Causes for Treatment Delays in Dystonia and Hemifacial Spasm: A Canadian Survey
Bibliographic record
Abstract
BACKGROUND: Dystonia must be accurately diagnosed so that treatment can be administered promptly. However, dystonia is a complex disorder, with variable presentation, which can delay diagnosis. METHODS: Data were gathered by questionnaire from 866 patients with dystonia or hemifacial spasm (HFS) treated in 14 movement disorders centres in Canada injecting botulinum toxin, to better understand the path to diagnosis, wait times and obstacles to treatment. RESULTS: Most participants were female (64.1%), mean age was 58 years, and patients consulted an average of 3.2 physicians before receiving a dystonia or HFS diagnosis. Many patients (34%) received other diagnoses before referral to a movement disorders clinic, most commonly "stress" (42.7%). A variety of treatments were often received without a diagnosis. The mean lag time between symptom onset and diagnosis was 5.4 years. After the decision to use botulinum toxin, patients waited a mean of 3.1 months before treatment. The most common diagnoses were cervical dystonia (51.6% of patients), HFS (20.0%) and blepharospasm (9.8%). CONCLUSIONS: Survey results show that diagnosis of dystonias or of HFS, and therefore, access to treatment, is delayed. An educational program for primary care physicians may be helpful to decrease the time to diagnosis and referral to a specialist centre for treatment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".