Clinical Improvement After Treatment With IncobotulinumtoxinA (XEOMIN®) in Patients With Cervical Dystonia Resistant to Botulinum Toxin Preparations Containing Complexing Proteins
Bibliographic record
Abstract
This study investigated the clinical long-term effect of incobotulinumtoxinA (incoBoNT/A) in 33 cervical dystonia (CD) patients who had developed partial secondary therapy failure (PSTF) under previous long-term botulinum toxin (BoNT) treatment. Patients were treated four times every 12 weeks with incoBoNT/A injections. Physicians assessed treatment efficacy using the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) at the baseline visit, week 12 and 48. Patients rated quality of life of CD with the Craniocervical Dystonia Questionnaire (CDQ-24). Titres of neutralizing antibodies(NAB) were determined at start of the study and after 48 weeks. All patients had experienced significant and progressive worsening of symptoms in the last 6 months of previous BoNT treatment. Repeated incoBoNT/A injections resulted in a significant reduction in mean TWSTRS at week 12 and 48. Patients' rating of quality of life was highly correlated with TWSTRS but did not change significantly over 48 weeks. During the 48 weeks -period of incoBoNT/A treatment NAB titres decreased in 32.2%, did not change in 45.2%, and only increased in 22.6% of the patients. Thus, repeated treatment with the low dose of 200 MU incoBoNT/A over 48 weeks provided a beneficial clinical long-term effect in PSTF and did not booster titres of NAB.
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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".