Psychometric evidence of spasticity measurement tools in cerebral palsy children and adolescents: A systematic review
Bibliographic record
Abstract
OBJECTIVE: To conduct a systematic review using validated critical appraisal scales to analyze both the quality and content of the psychometric evidence of spasticity measurement tools in cerebral palsy children and adolescents. DATA SOURCES: The literature search was performed in 3 databases (Pubmed, CINAHL, Embase) up to March 2012. STUDY SELECTION: To be retained for detailed review, studies had to report on at least one psychometric property of one or many spasticity assessment tool(s) used to evaluate cerebral palsy children and adolescents. DATA EXTRACTION: Two raters independently reviewed admissible articles using a critical appraisal scale and a structured data extraction form. DATA SYNTHESIS: A total of 19 studies examining 17 spasticity assessment tools in cerebral palsy children and adolescents were reviewed. None of the reviewed tools demonstrated satisfactory results for all psychometric properties evaluated, and a major lack of evidence concerning responsiveness was emphasized. However, neurophysiological tools demonstrated the most promising results in terms of reliability and discriminating validity. CONCLUSIONS: This systematic review revealed insufficient psychometric evidence for a single spasticity assessment tool to be recommended over the others in pediatric and adolescent populations.
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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 | 0.027 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".