Adolescent idiopathic scoliosis detection and referral trends: impact treatment options
Bibliographic record
Abstract
STUDY DESIGN: Retrospective cross-sectional study. OBJECTIVE: To analyze the patient demographic referred for scoliosis to the Hospital for Sick Children to determine the proportion of patients suitable for brace treatment, as per the Scoliosis Research Society guidelines. There is level 1 evidence that bracing in adolescent idiopathic scoliosis (AIS) decreases the risk of curve progression and need for surgery, but optimal brace treatment requires early curve detection. METHODS: We performed a retrospective review of 618 consecutive patients who underwent initial assessment in our Spine Clinic between Jan. 1 and Dec. 31, 2014. We included children 10-18 years, with scoliosis greater than 10°, excluding those diagnosed with non-idiopathic curves. Primary outcomes were Cobb angle, menarchal status, and Risser score. We analyzed the effect of specific referral variables (family history, the person who first noticed the curve, and geographic location of residence) on presenting curve magnitude. RESULTS: During the study period, 335 children met the inclusion criteria, with an average age of 14.1 ± 1.8 years and a mean Cobb angle of 36.8 ± 14.5°. Brace treatment was indicated in 17% of patients; 18% had curves beyond optimal curve range for bracing (> 40°), and 55% were skeletally mature, therefore not brace candidates. The majority of curves (54%) were first detected by the patient or family member and averaged 7° more than curves first detected by a physician. A family history of scoliosis made no difference to curve magnitude, nor did geographic location of residence. CONCLUSION: The majority of AIS patients present too late for effective management with bracing. LEVEL OF EVIDENCE: III.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".