Pioneering Experience of Uniportal Video-Assisted Thoracoscopic Surgery for Anterior Release of Severe Thoracic Scoliosis
Bibliographic record
Abstract
The optimal way to treat severe thoracic scoliosis remains controversial. Compared with conventional procedures, the uniportal video-assisted thoracoscopic surgery (UniVATS) rises in popularity in thoracic surgery because of less pain and faster recovery. This retrospective study aimed to apply UniVATS to treat severe thoracic scoliosis. Between October 2013 and March 2018, eight scoliotic patients with extremely large Cobb angle and profoundly limited flexibility underwent UniVATS for anterior release, followed by posterior instrumentation and fusion. The mean age at the time of surgery was 14.8 ± 2.4 years and the mean follow-up was 2.2 ± 1.3 years. The average levels of anterior thoracic discectomy and posterior fusion were 3.6 ± 0.7 and 11.5 ± 1.2, respectively. The mean coronal and sagittal correction rates were 70 ± 19% and 71 ± 23%, respectively. UniVATS contributed to minor access trauma (3-cm incision) with minimal blood loss, shorter operation time (75 ± 13 mins), less requirement of stay in the intensive care unit (0.3 ± 0.5 day) or chest tube placement (0.3 ± 0.7 day), speedier and narcotic-free recovery, and earlier ambulation within one day. This is the first study to assess the safety and efficacy of UniVATS in the treatment of severely stiff thoracic scoliosis, providing comparable surgical outcomes, less pain, faster recovery and superior cosmetic results without significant complications.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".