Pre-operative Halo-gravity Traction in the Treatment of Complex Spinal Deformities
Bibliographic record
Abstract
Spinal deformity surgeries are complex procedures associated with a high risk of complications. Halo-gravity traction (HGT) is a useful option that provides a gradual traction force to aid in deformity correction. Though its benefits are well-acknowledged, there still exist major ambiguities regarding its role in the management of complex spinal deformities. We performed a systematic review of the electronic databases including EMBASE, MEDLINE, PubMed, and Cochrane on November 12, 2021 to identify relevant articles on HGT; to analyze the existing literature on pre-operative HGT; and to compare the existing protocols for HGT in spinal deformity patients, its varied effects on the radiological parameters and general health status of the patients, and its associated complications. Among the 284 articles available in the literature, 34 articles were finally included and a total of 1151 patients [mean age, 14.6 years] were analyzed. Mean pre-traction coronal Cobb angle of 107° (72°–140.7°) was reduced by 24.8% [to a mean of 80.5° (42°–120.2°)] following HGT. Mean pre- and post-traction sagittal Cobb angles were 88° (56°–134.7°) and 65.4° (36°–113°; a reduction by 25.7%), respectively. Following HGT, mean body weight and body mass index (BMI) of patients improved by 7.2% and 9.1%, respectively. Mean improvement in forced vital capacity and forced expiratory volume has been reported to be 14.5% and 13.9%, respectively. Pre-operative HGT is a useful option in the treatment of spinal deformities. It aids in reducing curve magnitude and provides optimal time for improving general condition (pulmonary and nutritional status) of patients pre-operatively. It is a safe procedure with 2.1% neurological and 11.6% non-neurological complication rates.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".