Reliability of Clinical Diagnosis of Dystonia
Bibliographic record
Abstract
BACKGROUND: There is only one small single-center study on the reliability of the diagnosis of focal dystonia. The aim of this study was to assess the inter-rater reliability of dystonia diagnosis among neurologists with different professional experience. METHODS: Twenty-nine adults (18 with dystonia, 9 with other movement disorders, and 2 healthy controls) were videotaped while undergoing neurological examination and during the process of collecting information on the history of their condition. Each case was diagnosed by 35 blind raters (12 general neurologists, 21 neurology residents, and 2 experts in movement disorders) from different hospitals. Sensitivity and specificity were calculated confronting raters with the gold standard (the caring physician). Inter-rater agreement was measured by the Kappa statistic. RESULTS: Specificity and sensitivity were 95.2 and 66.7%, 76.3 and 75.2%, 84.6 and 71.6% for experts, general neurologists, and residents, respectively. Kappa values on dystonia diagnosis ranged from 0.30 to 0.46. The agreement was moderate for experts and residents (0.40-0.60) and fair for general neurologists (0.20-0.40). Kappas were the highest among experts for cranial and laryngeal dystonia (0.61-1), but not for cervical dystonia (0.37). CONCLUSIONS: The diagnosis of dystonia is difficult and only partially mirrors a physician's background.
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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 | 0.021 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".