A study validating the Italian version of the Level of Sitting Scale in children with cerebral palsy
Bibliographic record
Abstract
Objectives: To assess measurement properties of the Italian version of the Level of Sitting Scale when classifying sitting ability of children with cerebral palsy. Design: Methodological study. Setting: Children’s hospital (inpatients and outpatients). Subjects: Children 18 years of age or younger with cerebral palsy. Methods: The original English version of the Level of Sitting Scale was translated and culturally adapted for the Italian culture following international guidelines. Examination of reliability and validity of the Italian Level of Sitting Scale was then undertaken. Inter-rater and one-week test–retest reliability were estimated using both intraclass correlation coefficients (ICCs) with 95% confidence intervals and Bland–Altman plots. Construct validity of the Italian Level of Sitting was evaluated using three approaches examining Pearson’s correlation coefficient ( r) and Mann–Whitney U test ( P-value < 0.05). Main measures: Italian Level of Sitting Scale and Gross Motor Function Classification System. Results: The Italian Level of Sitting Scale was administered to 109 subjects. Inter-rater reliability and one-week test–retest showed excellent value with ICCs of 0.99 for both. (1) The Pearson correlation coefficient comparing Italian Level of Sitting Scale with Gross Motor Function Classification System was −0.91 and (2) correlation with total amount of adaptive seating components was −0.90. Differences in sitting abilities and use/non-use of wheelchair were found. All reported a statistical significance of P < 0.01. Conclusion: Our findings provide evidence of reliability and validity when using the Italian Level of Sitting Scale to classify seated postural abilities in a sample of Italian children with cerebral palsy.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".