Assessing the Severity of Cervical Dystonia: Ask the Doctor or Ask the Patient?
Bibliographic record
Abstract
Background: Assessing disease severity can be performed using either clinician-rated scales (CRS) or patient-rated outcome (PRO) tools. These two measures frequently demonstrate poor correlations. Objectives: To determine if the correlation between a CRS and PRO for motor features of cervical dystonia (CD) improves by accounting for non-motor features. Methods: Subjects with CD (N = 209) were evaluated using a CRS (Toronto Western Spasmodic Torticollis Rating Scale, TWSTRS) and a PRO (Cervical Dystonia Impact Profile, CDIP-58). Results: Linear regression revealed a weak correlation between the two measures, even when considering only the motor subscales of each. The strength of this relationship improved with a regression model that included non-motor symptoms of pain, depression, and disability. Conclusions: These results argue that the results of motor assessments in a PRO for CD cannot be fully appreciated without simultaneous assessment of non-motor co-morbidities. This conclusion might apply to other disorders, especially those with frequent non-motor co-morbidities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".