Impact of Various Weights in the Intraoperative Skull-Skeletal Traction on Correction of Adolescent Idiopathic Scoliosis
Bibliographic record
Abstract
Study Design: A retrospective study. Objectives: Intraoperative skull-skeletal traction (ISST) facilitates the surgical scoliosis correction, but it is also associated with neurological risk. The objective of the present study was to investigate the impact of various traction weights on neurophysiological change and curve correction in surgery for adolescent idiopathic scoliosis (AIS). Methods: A retrospective review of a consecutive series of posterior spinal fusions for AIS patients undergoing corrections with the use of ISST by 2 surgeons in one institution was performed. Intraoperative prone, post-traction radiographs were performed on all cases. The cases were divided into 2 groups, high and low traction weights, based on whether the weight used was ≥35% or <35% of body weight. The frequency of neurophysiological changes and the curve correction were compared between the 2 groups. Results: The intraoperative correction magnitudes by ISST were significantly larger in the high ISST group than in the low ISST group (35° vs 26°, P < .001). Changes in motor-evoked potential (MEP) were more frequently observed in the high ISST group (47% vs 26%, P = .049). A multivariate analysis showed that high ISST was associated with 3 times higher risk of MEP change (95% confidence interval = 1.1-8.0, P = .03) and higher final postoperative correction rates (68% vs 60%, P = .001). Conclusions: The high ISST for AIS was associated with increased intraoperative and ultimate curve corrections, and potentially facilitated better final correction. However, the high weight group was associated with an increased frequency of intraoperative MEP changes.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".