Measurement Properties of Clinical Scales Rating the Severity of Blepharospasm: A Multicenter Observational Study
Bibliographic record
Abstract
Background: Several scales have been proposed to clinically evaluate the Motor Severity of Blepharospasm (BSP) but information about their measurement properties as a multicenter instrument is limited. Objective: To compare the measurement properties of four clinical scales in rating the severity of BSP in a large sample of patients from multiple sites. Methods: The Burke-Fahn-Marsden Scale (BFMS), the Global Dystonia Severity Rating Scale (GDRS), the Jankovic Rating Scale (JRS), and the Blepharospasm Severity Rating Scale (BSRS) were administered to 211 patients across 10 sites who were also requested to self-complete the Blepharospasm Disability Index (BDI). Measurement properties to be assessed included inter-/intra-observer agreement, item-to-total correlation, internal consistency, floor and ceiling effect, convergent/discriminant validity, and adherence to the distribution of BDI. Results: The BFMS had unsatisfactory measurement properties, the GDRS had acceptable reliability but other properties could not be completely testable; the JRS had satisfactory measurement properties but the scale did not accurately reflect the distribution of disability parameter (BDI) in the sample, and the BSRS had satisfactory measurement properties and also showed the best adherence to the distribution of BDI in the assessed sample. Conclusion: The comparison of the measurement properties of four rating scales to assess the motor state of the BSP in a large sample of patients from multiple sites showed that the GDRS should be used to simultaneously assess BSP and dystonia in other body parts, while the JRS (easier to use) and BSRS (better to discriminate severity) should be used to assess BSP alone.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".