What Are the Indications for Spinal Fusion Surgery in Scheuermann Kyphosis?
Bibliographic record
Abstract
BACKGROUND: Surgical indications for Scheuermann kyphosis are variable. We sought to evaluate the characteristics of patients undergoing operative versus nonoperative treatment of Scheuermann kyphosis to better understand current practices and the factors which contribute to the decision for surgical management. METHODS: Multicenter prospective cohort study. We evaluated consecutive patients presenting with Scheuermann kyphosis. Patients underwent either surgical or nonoperative management according to surgeon and patient discretion. Preoperative patient-reported outcome measures (Scoliosis Research Society and Spinal Appearance Questionnaire scores), demographics, and radiographic characteristics were assessed. RESULTS: Overall, 150 patients with Scheuermann kyphosis were enrolled, with 77 choosing nonoperative treatment and 73 treated operatively. Compared with the nonoperative cohort, patients treated operatively were older (16.3±2.0 vs. 15.1±2.2, P=0.0004), and had higher body mass index (26.3±7.2 vs. 22.7±6.5, P=0.003), had greater T2-T12 kyphosis (71±14 degrees vs. 61±12 degrees, P<0.001), increased pelvic incidence (46 vs. 41 degrees, P=0.03) and pelvic tilt (10 vs. 3 degrees, P=0.03). There was no detected difference in maximal sagittal Cobb angle in the operative versus nonoperative patients (73±11 vs. 70±12 degrees, P=0.11). Functionally, the operative patients had worse Scoliosis Research Society pain scores (3.7±0.9 vs. 4.1±0.7, P=0.0027) and appearance scores (2.9±0.7 vs. 3.4±0.8, P <0.0001). CONCLUSIONS: Patients undergoing surgical management of Scheuermann disease were more likely to have large body mass index and worse pain scores. Other factors beyond radiographic measurement likely contribute to the decision for surgical management of Scheuermann kyphosis. LEVEL OF EVIDENCE: Level II.
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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 | 0.001 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".