Pilot feasibility study of a semi-automated three-dimensional scoring system for cervical dystonia
Bibliographic record
Abstract
The objective of this study is to test the feasibility of a semi-automated scoring system for the Toronto Western Spasmodic Torticollis Scale (TWSTRS) severity scale in patients with cervical dystonia. The TWSTRS requires training and experience. We previously developed a system to measure neck angle by analyzing three-dimensional position, obtained using Kinect, a marker-less three-dimensional depth sensor. The system can track patients' faces and bodies, automatically analyze neck angles, and semi-automatically calculate the TWSTRS severity scale score. We compared the TWSTRS severity scale scores calculated by the system with the video-based scores calculated by a neurologist trained in movement disorders. A correlation coefficient analysis was then conducted. Absolute accuracy was measured using intra-class correlation (ICC) (3,1), with 95% limits of agreement. To analyze the subscales, Cohen's kappa coefficient (κ) was calculated. A p-value of < .05 was considered statistically significant. Thirty patients were enrolled. Their average age was 52.3±16.0 years, and the male to female ratio was 3:2. The average disease duration was 11.3±12.7 years. Total score measurements by the system were significantly correlated with those rated by the movement disorder-trained neurologist (r = .596, p < .05). There was a significant correlation (r = .655, p < .05) with regard to the automated part of the scale. An adequate ICC (3,1) of .562 was obtained for total severity score (p < .001, 95% confidence interval [CI]: .259-.765), while the equivalent score was .617 for the total automated part (p < .001, 95% CI .336-.798). Our three-dimensional motion capture system, which can measure head angles and semi-automatically calculate the TWSTRS severity scale score utilizing a single-depth camera, demonstrated adequate validity and reliability. This low-cost and portable system could be applied by general practitioners treating cervical dystonia to obtain objective measurements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".