Bibliographic record
Abstract
Dystonia is the most common movement disorder in the pediatric population. It can affect normal motor development and cause significant motor disability. The treatment of pediatric dystonia can be very challenging as many children tend to be refractory to standard pharmacological interventions. Pharmacological treatment remains the first-line approach in pediatric dystonia. However, despite the widespread use of different ani-dystonia medications, the literature is limited to small clinical studies, case reports, and experts’ opinions. Botulinum neurotoxin (BoNT) is a well-established treatment in adults with focal and segmental dystonia. Despite the widespread use of BoNT in adult dystonia the data to support its use in children is limited with the majority extrapolated from the spasticity literature. For the last 2 decades, deep brain stimulation (DBS) has been used for a wide variety of dystonic conditions in adults and children. DBS gained increased popularity in the pediatric population because of the dramatic positive outcomes reported in some forms of genetic dystonia and the subsequent consensus that DBS is generally safe and effective. This review summarizes the available evidence supporting the efficacy and safety of pharmacological treatment, BoNT, and DBS in pediatric dystonia and provides practical frameworks for the adoption of these modalities.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".