Inter-rater reliability of the evaluation of muscular chains associated with posture alterations in scoliosis
Bibliographic record
Abstract
BACKGROUND: In the Global postural re-education (GPR) evaluation, posture alterations are associated with anterior or posterior muscular chain impairments. Our goal was to assess the reliability of the GPR muscular chain evaluation. DESIGN: Inter-rater reliability study. Fifty physical therapists (PTs) and two experts trained in GPR assessed the standing posture from photographs of five youths with idiopathic scoliosis using a posture analysis grid with 23 posture indices (PI). The PTs and experts indicated the muscular chain associated with posture alterations. The PTs were also divided into three groups according to their experience in GPR. Experts' results (after consensus) were used to verify agreement between PTs and experts for muscular chain and posture assessments. We used Kappa coefficients (K) and the percentage of agreement (%A) to assess inter-rater reliability and intra-class coefficients (ICC) for determining agreement between PTs and experts. RESULTS: For the muscular chain evaluation, reliability was moderate to substantial for 12 PI for the PTs (%A: 56 to 82; K: 0.42 to 0.76) and perfect for 19 PI for the experts. For posture assessment, reliability was moderate to substantial for 12 PI for the PTs (%A > 60%; K: 0.42 to 0.75) and moderate to perfect for 18 PI for the experts (%A: 80 to 100; K: 0.55 to 1.00). The agreement between PTs and experts was good for most muscular chain evaluations (18 PI; ICC: 0.82 to 0.99) and PI (19 PI; ICC: 0.78 to 1.00). CONCLUSIONS: The GPR muscular chain evaluation has good reliability for most posture indices. GPR evaluation should help guide physical therapists in targeting affected muscles for treatment of abnormal posture patterns.
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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 | 0.043 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".