An algorithm for determining scoliosis curve type according to Schroth
Bibliographic record
Abstract
To describe a refined classification algorithm, based on instructions provided during certification training, to unambiguously guide scoliosis curve type classification in a clinical trial. Schroth exercises are scoliosis specific [ 1 , 2 ]. They are the most researched and have been shown to lead to good outcomes. The Schroth classification consists of four mutually exclusive curve type categories (3c, 3cp, 4c and 4cp). Patients with scoliosis are classified according to their clinical presentation by a certified Schroth therapist. Observing the alignment of the following body blocks guides the classification assessment: lumbar spine and pelvis, thoracic spine and rib cage, and the cervical spine, head and shoulder girdle. Classifying patients’ curve types within the four Schroth curve categories determines the appropriate exercise prescription for a patient. An algorithm is needed to minimize errors in classifying different scoliosis patterns and help standardize exercise prescription.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".