Deep brain stimulation for pediatric dystonia: a meta‐analysis with individual participant data
Bibliographic record
Abstract
AIM: We performed a meta-analysis with individual participant data of deep brain stimulation (DBS) for dystonia in children and young people. METHOD: Three databases (PubMed, Embase, and Web of Science) were queried from January 1999 to August 2017 with no language restrictions to identify case studies and cohort studies reporting on pediatric patients (age ≤21y) with dystonia. The primary outcomes were changes in Burke-Fahn-Marsden (BFM) or Barry-Albright Dystonia Scale scores. A mixed-effects regression was used to identify associations between clinical covariates and outcomes. RESULTS: Of 2509 citations reviewed, 72 articles (321 children) were eligible. At last follow-up (median 12mo, 25th centile=9.0; 75th centile=32.2), 277 (86.3%) patients showed improvement in dystonia, while 66.1 percent showed clinically significant (>20%) BFM Dystonia Rating Scale-motor improvement. On multivariable hierarchical regression, older age at dystonia onset, inherited dystonia without nervous system pathology and idiopathic dystonia (vs inherited with nervous system pathology or acquired dystonia), and truncal involvement indicated a better outcome (p<0.05). INTERPRETATION: The data suggest that DBS is effective and should be considered in selected children with inherited or idiopathic dystonia. WHAT THIS PAPER ADDS: Deep brain stimulation is effective in selected children with inherited or idiopathic dystonia.
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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 | 0.023 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.047 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".