Comparing Clinical and Kinematic-Based Botulinum Toxin Injections for Cervical Dystonia Therapy
Bibliographic record
Abstract
BACKGROUND: Recognizing cervical dystonia (CD) movement patterns for appropriate botulinum toxin type A (BoNT-A) pattern determination depends on clinical expertise. Kinematic analysis objectively measures dystonic neck movements, and whether BoNT-A patterns determined solely using kinematics can effectively treat CD symptoms was investigated. METHODS: Twenty-two BoNT-A-naïve CD participants were randomized to receive three BoNT-A injections determined clinically ("cb") or by kinematic-based assessment ("kb"). Outcomes included the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) and kinematic measures of CD motor symptoms (tonic deviation and dynamic movements) at re-injection (weeks 12, 24) and peak effect (weeks 6, 18, 30) compared to baseline. RESULTS: Mean tonic deviation that returned to neutral was observed in 47% of "kb" and 31% of "cb" participants between weeks 6 and 30. Mean dynamic movements (root mean square amplitude) were significantly reduced in the "kb" group between weeks 12 and 30 compared to baseline. TWSTRS total score and motor severity were significantly reduced in the "cb" group, and disability sub-score was significantly reduced in both groups for all subsequent injections. Treatment-related side effects occurred in two "cb" and four "kb" participants. CONCLUSION: The study indicates that kinematic-based BoNT-A injection patterns can effectively reduce CD symptoms and disability, offering valuable guidance for both novice and experienced injectors.
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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".