Pain control due to botulinum toxin therapy in cervical dystonia relates to the sensorimotor integration process
Bibliographic record
Abstract
Background: Botulinum toxin (BoNT) injections have been found to improve pain symptoms of isolated cervical dystonia (CD). In addition to muscle relaxation at the peripheral level, few studies suggest that BoNT has effects on the central brain circuitries. The effects of BoNT on central circuitries that may be pain-related have not been examined. We probed these central effects with transcranial magnetic stimulation (TMS) techniques in a CD cohort presenting with significant pain. Methods: TMS-based measures of sensorimotor integration that are mediated through central processes, such as the short and long latency afferent inhibition (SAI and LAI) and measures for motor cortical excitability including short-interval intracortical inhibition (SICI) and intracortical facilitation (ICF) were recorded. These measures were recorded at specific interstimulus intervals (ISI) using paired-pulse paradigms before and after the peak effects of BoNT injections. Normative TMS data from age-matched healthy controls were collected for comparisons. Clinical pain symptoms were recorded with Toronto Western spasmodic rating scale (TWSTRS)-pain and a visual analog scale (VAS). Results: Eleven CD subjects (mean age ±SD, 53.1 ± 6.3 years) and 10 age-matched healthy controls were enrolled. SAI was found to be increased in CD patients at baseline, however at the time of peak BoNT effects, it revealed a significant change with normalization to healthy control data (SAI ISI 20 ms, p = 0.001; SAI ISI 30 ms, p = 0.03). The change in SAI correlated with improvements in pain levels assessed with TWSTRS-pain and VAS and the total dose of BoNT injected (corrected for multiple correlations). LAI, SICI, and ICF measures were similar to the healthy controls and remained unchanged with BoNT therapy. Conclusion: Pain control in CD from BoNT therapy relates to modulation of sensorimotor integration at the cortical level.
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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 | 0.001 |
| 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".