Intraoperative Traction in Scoliosis: A Safe and An Effective Tool to Achieve Better Correction
Bibliographic record
Abstract
Purpose: We believe that intraoperative skull-femoral traction (IOT) may effectively assist with spinal deformity correction.The aim of this study is to find out the effect of IOT in single-stage posterior arthrodesis for AIS and NM.Methods: A retrospective cohort study was performed after Institutional Review Board (IRB) approval.Inclusion criteria were Cobb's angle >50degrees, single stage posterior spinal instrumented fusion, follow-up >6 months.Growth-friendly surgeries were excluded.Group I consisted of patients with IOT while group II was without IOT.Results: Group I consisted of 35 patients with mean follow-up of 2.5 years (range 9 months to 6.3 years) and group II had 58 patients with a mean follow-up of 2.11 years (range 6 months to 6.6 years).Correction index was 11.1% more (p-value <0.05) in group I compared to group II.Mean blood loss and operative time were 662 ml (range 205 to 1513ml) and 7.14 hours (range 4.6 to 9.2 hours) in group I, while 647 ml (range 170 to 2200 ml) and 6.04 hours (range 4.1 to 10.2 hours) in group II.OR time was significantly more in group I.There was no statistical difference between the two groups in terms of flexibility index, complication rates, and blood loss.Neurophysiological changes were not seen in the traction group. Conclusion:We found the use of IOT is a safe and an effective tool to achieve better correction without an increase in complication rates and blood loss.
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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 | 0.001 |
| 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 | 0.000 |
| 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".