Ponte osteotomies increase risk of intraoperative neuromonitoring alerts in adolescent idiopathic scoliosis surgery
Bibliographic record
Abstract
Background: Ponte osteotomies (PO) are commonly used in adolescent idiopathic scoliosis (AIS) surgeries to improve the coronal and sagittal deformity correction. Here, we compared the incidence of perioperative neurologic complications for patients undergoing AIS with versus without PO. Methods: In a retrospective cohort study of 80 consecutive AIS patients undergoing scoliosis correction, 40 underwent PO, while 40 did not. All operations were performed by one surgeon at one tertiary care center. Patients’ demographics, Lenke classifications, surgical data, and deformity characteristics were comparable in both groups. Perioperative neurologic complications, defined as spinal cord or nerve root injuries identified by the surgeon, were tracked for those undergoing AIS surgery with or without PO being performed. Results: The risk of IOM alerts was significantly higher in the PO patients (12.5%: 5 patients) versus those in the No-PO group (0%, P = 0.021). Despite these changes, no patient incurred an increased postoperative deficit. Nevertheless, PO group patients demonstrated a higher coronal deformity correction rate (PO: 71% ± 10.9 vs. NoPO: 64.2% ± 11.5, P = 0.008) and a greater kyphosis Cobb angle (PO: 25.2 ± 6 vs. No-PO: 17.5 ± 9.4, P = 0.0001) on postoperative follow-up. Conclusion: While PO improved 3D correction of AIS, it increased the risk of IOM alerts in 12.5% of cases.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".