What Type of Incision for Anterior Cervical Spine Surgery Involving Long Segments Can Bring Better Cosmetic and Functional Outcomes?
Bibliographic record
Abstract
OBJECTIVE: To determine whether double transverse incisions could provide superior cosmetic and functional outcomes, including rates of dysphagia and dysphonia, compared with longitudinal incisions in patients undergoing anterior cervical spine surgery (ACSS) involving ≥ 3 levels. METHODS: A total of 62 consecutive patients who underwent ACSS involving ≥ 3 levels were included in this study. They consist of 33 with longitudinal incisions (L group) and 29 with double transverse incisions (DT group). We recorded functional outcome measures including the Bazaz score for postoperative dysphagia and the Voice Handicap Index-10 (VHI-10) for postoperative dysphonia. The Vancouver Scar Scale (VSS) and the patient and observer scar assessment scale (POSAS) were used to evaluate postoperative skin scarring. RESULTS: Cosmetic results, as assessed using the VSS and POSAS, were significantly better in the DT than in the L group at most follow-up time points (p < 0.01 each). Dysphagia rates were significantly lower in the DT group than in the L group during the late postoperative period from 6 months until final 2 years of follow-up (p < 0.01 each). There were no significant different results between the 2 groups in terms of dysphonia. CONCLUSION: A double transverse incision can be a feasible option when performing ACSS involving ≥ 3 levels, providing better cosmesis and lower rates of persistent dysphagia than with a longitudinal incision.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".