Responsiveness of the Seated Postural Control Measure and the Level of Sitting Scale in children with neuromotor disorders
Bibliographic record
Abstract
PURPOSE: Responsiveness of the Seated Postural Control Measure (SPCM) and the Level of Sitting Scale (LSS) was explored for children with neuromotor disorders. Total change scores for alignment (SPCM-A), function (SPCM-F) and sitting ability (LSS) were compared with a criterion change measure, the Global Change Scale (GCS). The a priori hypotheses predicted moderate correlations (r>0.40). METHOD: Both SPCM and LSS were administered twice, 6 months apart. Parents and two therapists rated changes in alignment and function, and indicated importance of those changes on the GCS. Participants (n=114) were divided into two groups: those whose posture was expected to change, (with a range of diagnoses) and those who were expected to remain stable (with a diagnosis of cerebral palsy). Ages ranged from 1 to 18 years. RESULTS: Fair-to-moderate significant correlations (p ≤0.01) were found between SPCM-F and LSS change scores and parents' and therapists' rating of change and importance of change on the GCS. Correlations for SPCM-A change scores were insignificant. The standardised response mean values for SPCM-F and LSS confirmed a minimal clinically important difference. CONCLUSIONS: SPCM-F shows promise as a responsive outcome measure, however; SPCM-A requires further work. LSS may be useful for evaluative purposes, in addition to its role as a classification index.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".