Validation of clinical ratings of cervical dystonia using computer-generated avatars
Bibliographic record
Abstract
INTRODUCTION: The Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) and the Collum-Caput (Col-Cap) concept are tools for clinically assessing cervical dystonia severity. However, the accuracy of human ratings using these scales has not been systematically evaluated due to the lack of objective reference measurements. This study aims to assess and compare the accuracy of human TWSTRS and Col-Cap ratings to evaluate their robustness for clinical and research applications. METHODS: One hundred pictures of 26 avatars mimicking cervical dystonia were created using the Rocketbox Avatar library. Forty-one movement disorder specialists rated the head and neck positioning of the avatars using either TWSTRS or Col-Cap. Movements were defined around two rotational levels (head, neck) in three rotational axes (pitch, yaw, roll). RESULTS: Ratings of angular deviations showed a mean absolute error of 5.8° (SD = 7.0). Rating accuracy was primarily influenced by the magnitude of angular deviation, with larger angles leading to greater estimation errors. Direct comparison of the rating scales revealed a higher accuracy through Col-Cap ratings (71 % vs. 63 % for TWSTRS). Years of clinical experience did not significantly affect rating accuracy. CONCLUSIONS: Both rating systems (TWSTRS and Col-Cap) show moderate accuracy in assessing head and neck positioning from computer-generated avatars, with Col-Cap showing slightly higher overall accuracy but struggling with precise differentiation between head and neck movements. These findings underscore the limitations of current clinical rating scales and highlight the need for more objective, reliable tools to effectively assess cervical dystonia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".