Surgical Decision Making in Adolescent Idiopathic Scoliosis
Bibliographic record
Abstract
In Brief Study Design. Cross-sectional survey. Objective. The objective of this paper was to assess the reliability of surgeons’ decision-making in adolescent idiopathic scoliosis (AIS) based on patient photographs and clinical and radiographic data. Summary of Background Data. Orthopedic spine surgeons rated severity of deformity as the most important surgical consideration in AIS. However, studies have shown that surgeon reliability is highly variable when rating physical deformity. Surgeons’ unreliable ratings of patients’ physical deformity may lead to inconsistent decision-making. Methods. Four pediatric spine surgeons viewed 40 patients with varying severity of AIS on three occasions, 2 weeks apart. In the first viewing, surgeons viewed only patient photos and body image scores. In the second viewing, surgeons viewed patient photos, body image scores, and clinical data. In the third viewing, surgeons viewed patient photos, body image scores, a 3-ft anteroposterior spinal radiograph, and corresponding radiographic data. After viewing each patient, surgeons were asked if: 1) spinal fusion with or without thoracoplasty would improve the patient’s appearance; and 2) whether they would recommend this patient for spinal fusion with or without thoracoplasty. Results. Surgeons’ concordance in recommending patients for surgery and if they thought it would improve their appearance varied widely with kappa scores ranging from poor (0.34) to good (0.76). Recommendations for surgery were more consistent with the addition of radiographs but were not influenced by patients’ body image perceptions. Surgeons’ recommendations for surgery were also inconsistent with treatment actually received with overall kappa scores ranging from poor (0.32) to good (0.73). Conclusion. Surgical decision-making for AIS is inconsistent. The objective of this paper was to assess the reliability of surgeons’ decision-making in adolescent idiopathic scoliosis based on patient photographs and clinical and radiographic data. Recommendations for surgery were more consistent with the addition of radiographs but were not influenced by patients’ body image perceptions. Surgeons’ recommendations for surgery were also inconsistent with treatment actually received.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".