Identification and measurement of dystonia in cerebral palsy
Bibliographic record
Abstract
AIM: To establish the prevalence and severity of dystonia in a population of children with cerebral palsy (CP) with hypertonia assessment and measurement tools. METHOD: A cross-sectional study of 151 children (84 males, 67 females) with CP who were assessed with the Hypertonia Assessment Tool (HAT) and Barry-Albright Dystonia scale (BAD) for identification and measurement of severity of dystonia. HAT dystonia items were assessed for construct and convergent validity. RESULTS: Distribution by predominant motor type (PMT) was: 85% spastic, 14% dyskinetic, and 1% ataxic. Spastic and dyskinetic groups showed widespread evidence of dystonia according to HAT profiles and BAD scores. The dyskinetic PMT group had a higher mean BAD score than the spastic group (difference of 13 units, 95% CI 9.1-16.4). Dystonia severity (BAD score) increased linearly across gross motor (p<0.001), manual ability (p<0.001) and communication functional levels (p<0.001). Divergence was noted in how HAT item six identified dystonia compared to items one and two. INTERPRETATION: The HAT provided an estimate of the prevalence of both spasticity and dystonia in a large CP population, beyond predominant motor type. Dystonia is a common finding in the spastic PMT group, and its severity increases as motor function worsens. WHAT THIS PAPER ADDS: Dystonia is readily identified in cerebral palsy (CP) using the Hypertonia Assessment Tool, regardless of the predominant motor type. Spasticity and dystonia frequently coexist in the CP population. Severity of dystonia is inversely related to motor function.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".