Impact of Seasonal Variations on Spasticity Assessment and Treatment
Bibliographic record
Abstract
Background: Spasticity is treated using intramuscular botulinum toxin injections. Previous reports of worsening of spasticity in cold weather suggest that spasticity levels and botulinum toxin dose may both be influenced by seasonal temperature changes. AIMI: The aim of this study was to determine the effect of seasonal temperature variation on spasticity and botulinum toxin dosage. Methods: Medical charts of 132 participants in 3 diagnostic groups: (1) stroke (n=60); (2) multiple sclerosis (n=34); and (3) cerebral palsy (n=38) receiving treatment in an outpatient spasticity clinic were reviewed. Age, sex, diagnosis, number of muscles injected, botulinum toxin dose, and spasticity (Modified Ashworth Scale) were collected for summer and winter. Spasticity and botulinum toxin dose were compared between summer and winter using separate paired t-tests for each diagnostic group. Results: The spasticity level, botulinum toxin dosage, and total number of muscles in summer versus winter months were not significantly different (p>0.05) in any diagnostic group. Conclusions: Seasonal temperature variations appear to have no effect on spasticity or botulinum toxin dosage. Performance of spasticity measurements in warm indoor environment may have negated the impact of seasonal temperature differences. Future studies should perform spasticity measurements in temperature controlled rooms to mimic seasonal variations.
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 | 0.000 | 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".