Development of the Hypertonia Assessment Tool (HAT): a discriminative tool for hypertonia in children
Bibliographic record
Abstract
AIM: The aim of this study was to develop a tool to identify paediatric hypertonia subtypes. METHOD: Items generated by experts were subscaled (spasticity, dystonia, rigidity). The tool was administered to 34 children (19 males, 15 females, mean age 8y 2mo, range 2y 5mo-18y 7mo) with hypertonia and cerebral palsy (CP) in Gross Motor Function Classification System (GMFCS) levels: I, n=7; II, n=5; III, n=7 level IV, n=7; and level V, n=8 level. Kuder-Richardson Formula 20 determined internal consistency. To assess reliability, two physicians administered the tool to 25 additional children with CP (15 males, 10 females; mean age 10y 8 mo; GMFCS levels I, n=4; II, n=3; III, n=7; IV, n=4; and V, n=7) on two occasions, 2 weeks apart. To evaluate validity, a third physician diagnosed the hypertonia by neurological examination. RESULTS: The internal consistency of the spasticity items was moderate (alpha = 0.58), and dystonia was high (a=0.79). Item reduction eliminated seven of the 14 original items. The agreement of the spasticity and rigidity subscales was adequate (prevalence-adjusted bias-adjusted kappa [PABAK] ranging from moderate [0.57] to excellent [1.0]) for validity, test-retest reliability, and interrater reliability. For dystonia agreement was lower, with PABAK ranging from fair (0.30) to good (0.65). Eighty-seven per cent had spasticity and 78% had dystonia. INTERPRETATION: The Hypertonia Assessment Tool has good reliability and validity for identifying spasticity and the absence of rigidity, and moderate findings for dystonia.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".