Low transverse incision for lateral neck dissection in patients with papillary thyroid cancer: improved cosmesis
Bibliographic record
Abstract
BACKGROUND: Various incisions and approaches have been developed for lateral neck dissection. The purpose of this study was to compare the surgical and cosmetic outcomes of a single low transverse incision with the hockey stick incision for lateral neck dissection (LND) in patients with papillary thyroid carcinoma (PTC). METHODS: We retrospectively analyzed 97 patients with PTC who underwent therapeutic LND and total thyroidectomy by low transverse incision (62 patients) or hockey stick incision (35 patients). We compared the operative results, cosmetic outcomes, objective scar measurement, and sensory disturbance between the two groups. RESULTS: The number of harvested and metastatic lymph nodes, Vancouver Scar Scale scores, and sensory change were not significantly different between the two groups. The mean number of harvested lymph nodes in level II was 9.82 vs. 9.63 (P = 0.885) (transverse incision vs. hockey stick incision, respectively) and in level V was 6.36 vs. 5.63 (P = 0.597). However, subjective satisfaction with the scar and neck contour was higher in the low transverse incision group compared with the hockey stick incision group. Scores for scar consciousness and sensory change were not significantly different between the two groups. CONCLUSIONS: A single low transverse incision may provide equivalent surgical outcomes and superior cosmetic outcomes compared with the hockey stick incision for LND in PTC.
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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 | 0.000 | 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".