Relationship Between Botulinum Toxin, Spasticity, and Pain: a Survey of Patient Perception
Bibliographic record
Abstract
OBJECTIVE: To assess the prevalence of pain in adults with spasticity and to assess the association between the subjective experience of pain and spasticity. DESIGN: Cross-sectional study. SETTING: outpatient spasticity management clinic of a rehabilitation centre. PATIENTS: Patients with upper motor neuron lesions and spasticity (n=131) were recruited. METHODS: We assessed pain intensity and location, relationship between spasticity and pain perception, and perception of pain relief from botulinum toxin type-A (BoNTA) injections. MAIN OUTCOME MEASURES: Pain perception rated on a 10-point numerical rating scale and pain quality. RESULTS: 65% of the patients with spasticity reported presence of pain and 60% described it as an aching pain. More patients reported pain with movement (34%) compared to rest (21%). There was a statistically poor correlation between the severity of pain and spasticity (r=0.16; p>0.05). Most patients (80%) believed that their pain was related to spasticity and 62% reported that BoNTA injections decreased their pain. CONCLUSIONS: The high incidence of pain noted within our sample suggests that physicians may have to consider pain management as part of spasticity treatment. Participants reported that their pain was related to their spasticity, and that it decreased after BoNTA treatment. Further study is needed to explore the relationships between objective measures of spasticity and pain.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".