PREDICT-ITB: Predicting response in children with dystonic CP to ITB – study protocol
Bibliographic record
Abstract
Introduction: Over 11,000 infants are diagnosed with cerebral palsy (CP) each year, with lifetime medical costs exceeding $1.4 million per person. Elevated muscle tone in CP, including dystonia and spasticity, significantly impairs function and quality of life. Intrathecal baclofen (ITB) is commonly used to manage dystonic CP, though evidence supporting its effectiveness is weak due to patient variability and study limitations. Spasticity may obscure dystonia symptoms, and factors like brain injury patterns and pain triggers are often overlooked in research. Despite uncertain effects on dystonia, ITB has shown benefits in pain relief, comfort, and caregiving ease. This proposal aims to evaluate ITB's overall impact on children with dystonic CP, identify responders, and develop a comprehensive outcome measure using a prospective cohort study. Methods and Analysis: We will conduct a prospective, observational study of 65 children with dystonia (Barry Albright Dystonia Scale (BADS) greater than 15) and CP who receive ITB. Changes in dystonia, spasticity, gross and fine motor function, and multiple patient-reported outcomes related to quality-of-life, depression, anxiety, pain and more. The primary analysis will use repeated measures models to estimate short and long-term changes from baseline in BADS scores at 3, 6 and 12-months. Secondary analysis will apply the same strategies to the other outcome measures. We will also conduct subgroup analysis and develop a multidimensional or composite measure. Ethics and dissemination: Primary ethic approval was provided by the Baylor College of Medicine Institutional Review Board (H-54449). Results of the study will be disseminated via peer-reviewed presentations at scientific conferences and open access publication. Trial Registration Number: NCT06606574 ( clinicaltrials.gov ). STRENGTHS AND LIMITATIONS: Unlike previous studies on ITB in dystonia and CP, a strength of this study is that it will directly measure effects of ITB beyond just dystonia, while also considering the child's co- existing spasticity if present, known triggers of dystonia, including pain, and CNS injury patterns contributing to dystonia.We consider multiple endpoints or the "total child" within the ICF Framework and whether concurrent therapeutic interventions appear to influence outcomes.A limitation of the study is the lack of randomization to placebo or blinding.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".