A new method for the evaluation of cervical dystonia
Bibliographic record
Abstract
Objectives: The study aimed to develop a measurement method that yielded objective data for the clinical assessment of cervical dystonia using a Kinect camera system. Patients and methods: This double-blind, parallel-group method development study included 22 patients with cervical dystonia (3 males, 19 females; mean age: 47 years; range, 34 to 60 years) and 20 healthy individuals (13 females, 7 males; mean age: 32 years; age range, 22 to 65). Using cameras and a computer software, the head-neck postures of 40 healthy participants were recorded in the virtual environment. Using the device, 22 patients with cervical dystonia were examined both at rest and while moving with different parts of the body. Two different experts evaluated and scored the cases using the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) and the Tsui scale. Results: A three-way comparison revealed interclass correlations between the coefficients of 0.799 (79.9%) and 0.784 (78.4%) for at rest and with movement, respectively. The two-way comparison of the experts revealed correlation coefficients of 0.717 (71.7%) and 0.692 (69.2%) for at rest and with movement, respectively. A three-way comparison of the device and Expert 1 and Expert 2 TWSTRS scores revealed interrater agreement values of 0.6 and 0.8 (good) and 0.6 and 0.8 (good) while at rest and with movement, respectively. A three-way comparison of the device and Expert 1 and Expert 2 Tsui scores revealed interrater agreement values of 0.6 and 0.8 (good) and 0.4 and 0.6 (moderate) while at rest and with movement, respectively. Conclusion: The newly developed system was a sensitive tool for use in the kinematic evaluation of patients with cervical dystonia and could prove beneficial in diagnosis and treatment follow-up.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".