Pain relief in cervical dystonia following regular long-term botulinum therapy
Bibliographic record
Abstract
Introduction. Cervical dystonia (CD) is characterized by pain, which is often the main reason for visiting a doctor. Analgesics are often used to control pain, but regular administration of botulinum toxin type A (BTA) may be more effective. The effectiveness of regular long-term use of BTA in CD in relation to pain has been little studied, which served as the basis for this study. Material and methods. For 3 years, 65 patients (44 men, 21 women, average age 53±15 years) with CD who regularly received BTA therapy were observed. The severity of CD was assessed using the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS), pain — using TWSTRS and a Numeric Rating Scale (NRS) at baseline, one month after BTA therapy and 3 years later during the period of maximum severity of symptoms (before the next BTA injection). Results. A decrease in pain according to NRS and TWSTRS was shown not only after 1 month, but also after 3 years (p<0.0001). The reduction in pain was significant for initially moderate and severe pain according to the NRS of pain (p<0.0001). In patients with mild pain intensity, no significant changes were noted (F=1.5, p=0.23). An inverse correlation was noted between the sensory trick and the duration of the disease. Conclusion. The use of BTA for CD reduces pain not only after a month, but also after 3 years of regular therapy during the period of maximum severity of symptoms.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 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".