Towards new perspectives: International consensus guidance on dystonia in pediatric palliative care
Bibliographic record
Abstract
BACKGROUND: Pediatric dystonias are associated with a broad spectrum of etiologies, resulting in a heterogeneous patient population in whom clinical presentation, evolution, and therapeutic needs may differ. These neurological symptoms are particularly common in children and adolescents with life-limiting and life-threatening conditions requiring pediatric palliative care (PPC). The impact on the child's quality of life is significant, as is distress for caregivers. Addressing and alleviating dystonia is key to providing good palliative care; however, there is limited evidence. A greater recognition and management of dystonia in this setting is urgently needed to provide appropriate interventions and care. OBJECTIVES: To develop a standardized approach to dystonia in PPC. MATERIALS AND METHODS: A two-round Delphi process explored the views of experts on the definition, assessment, monitoring, and treatment of dystonia in PPC. Professionals from different backgrounds and disciplines were invited worldwide. The final panel comprised 71 participants who completed a multi-statement online questionnaire. RESULTS: Fifty-three items were endorsed, providing expert, consensus-based recommendations. CONCLUSIONS: The limited clinical knowledge of childhood dystonia represents a challenge, especially in children with palliative care needs. This study is a first international consensus on dystonia in PPC and offers novel approaches to improving the dystonia-related burden and advancing clinical practice in this vulnerable population.
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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 | 0.186 | 0.237 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.011 | 0.023 |
| Research integrity | 0.030 | 0.032 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".