Clinical phenotypes associated with outcomes following deep brain stimulation for childhood dystonia
Bibliographic record
Abstract
OBJECTIVE: Although deep brain stimulation (DBS) is an accepted treatment for childhood dystonia, there is significant heterogeneity in treatment response and few data are available to identify ideal surgical candidates. METHODS: Data were derived from a systematic review and individual patient data meta-analysis of DBS for dystonia in children that was previously published. Outcomes were assessed using the Burke-Fahn-Marsden Dystonia Rating Scale for movement (BFMDRS-M) and for disability (BFMDRS-D). The authors used partial least squares, bootstrapping, and permutation statistics to extract patterns of contributions of specific preoperative characteristics to relationship with distinct outcomes, in all patients and in patients with primary and secondary dystonia separately. RESULTS: Of 301 children undergoing DBS for dystonia, 167 had primary dystonia, 125 secondary dystonia, and 9 myoclonus dystonia. Three dissociable preoperative phenotypes (latent variables) were identified and associated with the following: 1) BFMDRS-M at last follow-up; 2) relative change in BFMDRS-M score; and 3) relative change in BFMDRS-D score. The phenotype of patients with secondary dystonia, with a high BFMDRS-M score and truncal involvement, undergoing DBS at a younger age, was associated with a worse postoperative BFMDRS-M score. Children with primary dystonia involving the trunk had greater improvement in BFMDRS-M and -D scores. Those with primary dystonia of shorter duration and proportion of life with disease, undergoing globus pallidus DBS, had greater improvements in BFMDRS-D scores at long-term follow-up. CONCLUSIONS: In a comprehensive, data-driven, multivariate analysis of DBS for childhood dystonia, the authors identified novel and dissociable patient phenotypes associated with distinct outcomes. The findings of this report may inform surgical candidacy for DBS.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".