The Effect of Neural Lesion Type on Botulinum Toxin Dosage: A Retrospective Chart Review
Bibliographic record
Abstract
BACKGROUND: It is difficult to compare the dosage of botulinum toxin between different neurologic conditions because of the different methods of reported dosages. Botulinum toxin is used to manage spasticity in variety of neurologic conditions, and it is important for clinicians to know whether there are differences in the dosage injected on the basis of the etiology of spasticity. OBJECTIVE: To determine whether the type of neural lesion influences the dosage of botulinum toxin required to manage spasticity. DESIGN: Retrospective chart review. SETTING: Review of patients who visited an outpatient spasticity clinic. PARTICIPANTS: We assessed medical charts from 99 patients with stroke, multiple sclerosis (MS), and cerebral palsy (CP) (n = 33 for each etiology). We collected information such as age, gender, weight, time of lesion, total dosage (per person, per limb, per muscle), injection location, and injections cycles. INTERVENTIONS: None. MAIN OUTCOME MEASUREMENTS: OnabotulinumtoxinA dose - total dose in one leg was calculated as a sum of the units of the toxin injected in all the leg muscles. RESULTS: Total dose of toxin injected was 161 ± 19 (mean ± standard error of mean) in patients with stroke, 175 ± 13 in patients with CP, and 225 ± 18 in patients with MS. The total dose in the legs (normalized to body weight; units/kg) was significantly different between the 3 groups (stroke, CP, MS; P = .001). Subsequent post-hoc tests revealed that total dose in the legs of patients with MS was significantly greater (88%) than patients with stroke (P = .001). Hip adductors and hamstrings were injected most commonly in MS and CP, but toe muscles were commonly injected in patients with stroke, whereas plantar flexors were evenly injected all 3 patient groups. CONCLUSION: In our practice, we found that treating spasticity in people with MS required the greatest dose of botulinum toxin, followed by CP and then stroke.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 0.001 |
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 teacher head, 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".