Pharmacological and neurosurgical interventions for managing dystonia in cerebral palsy: a systematic review
Bibliographic record
Abstract
AIM: To systematically review evidence for pharmacological/neurosurgical interventions for managing dystonia in individuals with cerebral palsy (CP) to inform a care pathway. METHOD: Searches included studies with a minimum of five participants with dystonia in CP receiving oral baclofen, benzodiazepines (clonazepam, diazepam, lorazepam), clonidine, gabapentin, levodopa, trihexyphenidyl, botulinum toxin, intrathecal baclofen (ITB), or deep brain stimulation (DBS). Evidence was classified according to American Academy of Neurology guidelines. RESULTS: Twenty-eight articles underwent data extraction: one levodopa, five trihexyphenidyl, three botulinum toxin, six ITB, and 13 DBS studies. No articles for oral baclofen, benzodiazepines, clonidine, or gabapentin met the inclusion criteria. Evidence for reducing dystonia was level C (possibly effective) for ITB and DBS; level C (possibly ineffective) for trihexyphenidyl; and level U (inadequate data) for botulinum toxin. INTERPRETATION: For dystonia reduction, ITB and DBS are possibly effective, whereas trihexyphenidyl was possibly ineffective. There is insufficient evidence to support oral medications or botulinum toxin to reduce dystonia. There is insufficient evidence for pharmacological and neurosurgical interventions to improve motor function, decrease pain, and ease caregiving. The majority of the pharmacological and neurosurgical management of dystonia in CP is based on clinical expert opinion. WHAT THIS PAPER ADDS: Intrathecal baclofen and deep brain stimulation are possibly effective in reducing dystonia. Current evidence does not support effectiveness of oral medications or botulinum toxin to reduce dystonia. Evidence is inadequate for pharmacological/neurosurgical interventions impact on improving motor function, pain/comfort, and easing caregiving. The majority of the care pathway rests on expert opinion.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".