Seated postural control measure: Italian translation and validation in children with cerebral palsy
Bibliographic record
Abstract
BACKGROUND: Seating interventions are part of the postural management program recommended for children with Cerebral Palsy (CP) who are nonambulatory or have difficulty in walking. The lack of validated outcome measures for assessing seating interventions limits access to obtain valid and comparable data internationally. OBJECTIVE: To translate the Seated Postural Control Measure (SPCM) into Italian and assess reliability and validity of the translated measure in a population of children with CP. STUDY DESIGN: Cross-sectional and repeated measures study. METHODS: The original version of the SPCM was translated and culturally adapted by a multidisciplinary team according to international guidelines. Internal consistency was examined with Cronbach's alpha. Both inter- and intrarater reliabilities were estimated using intraclass correlation coefficients with 95% confidence intervals. Intrarater reliability was estimated over a 1-week period. Convergent construct validity was investigated by comparing SPCM values with the Level of Sitting Scale, the Gross Motor Function Classification System, and the Manual Ability Classification System using Pearson's correlation coefficients. RESULTS: The Italian version of the SPCM (IT-SPCM) was evaluated on 72 children with CP aged 4-18 years. Cronbach's alpha was 0.83, 0.95, and 0.93 for IT-SPCM alignment subscale, IT-SPCM functioning subscale, and total score, respectively. Reliability for IT-SPCM total score was high for both inter- and intrarater reliabilities (0.95 and 0.93). The IT-SPCM total score was moderately correlated with the Level of Sitting Scale (0.37), Gross Motor Function Classification System (-0.41), and Manual Ability Classification System (-0.56). CONCLUSION: Preliminary evidence supports the reliability and validity of using IT-SPCM with a population of children with CP.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".