Algorithm for Schroth-Curve-Type Classification of Adolescent Idiopathic Scoliosis: An Intra- and Inter-Rater Reliability Study
Bibliographic record
Abstract
Schroth exercises for scoliosis are prescribed based on curve types. This study aimed to determine the reliability of an algorithm for classifying Schroth curve types. Forty-four consecutive volunteers with adolescent idiopathic scoliosis, 10 to 18 years old, with curves 10° to 50°, were recruited from a scoliosis clinic. Their standing posture and Adam's bending test were videotaped. Ten consecutive Schroth therapist volunteers from an international registry independently classified the curve types using the proposed classification algorithm. Videos were rated twice at least seven days apart. Reliability was calculated using the Gwet's AC1 agreement coefficient for all the raters and for subgroups reporting full understanding (well-trained) and with prior algorithm experience. The intra-rater and weighted agreement coefficients for all the raters were 0.64 (95% CI: 0.53-0.73) and 0.75 (0.63-0.84), respectively. For the well-trained raters, they were 0.70 (0.60-0.78) and 0.82 (0.73-0.88), respectively; for the experienced raters, they were 0.81 (0.77-0.85) and 0.89 (0.80-0.94), respectively. The inter-rater versus weighted agreement coefficients for all the raters were 0.43 (0.28-0.58) versus 0.48 (0.29-0.67). For the well-trained raters, they were 0.50 (0.38-0.61) versus 0.61 (0.49-0.72), and for the experienced raters, they were 0.67 (0.50-0.85) versus 0.79 (0.64-0.94). Full understanding and experience led to higher reliability. Use of the algorithm can help standardize Schroth exercise treatment.
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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 | 0.035 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".