Long-Term Cosmetic Outcomes After Robotic/Endoscopic Thyroidectomy by a Gasless Unilateral Axillo-breast or Axillary Approach
Bibliographic record
Abstract
BACKGROUND: Although the excellent short-term cosmesis after robotic/endoscopic thyroidectomy has been reported, the long-term cosmetic outcome is not yet known. The aim of this study was to evaluate the long-term cosmetic outcome of robotic/endoscopic thyroidectomy. PATIENTS AND METHODS: We compared 147 patients who underwent robotic or endoscopic thyroidectomy using a gasless unilateral axillo-breast (GUAB) approach or a gasless unilateral axillary (GUA) approach with 161 conventional open thyroidectomy patients. Subjective cosmetic outcomes were evaluated using a series of scar-specific questions as well as the Vancouver scar scale at 12-18 months after surgery. The cosmetic satisfaction score was defined as the sum of the two cosmetic satisfaction questions with a rating scale of 1-5. The scar consciousness score was defined as the sum of the four scar consciousness questions with a rating scale of 0-3. RESULTS: The cosmetic satisfaction and scar consciousness scores were significantly better in the robotic/endoscopic group than in the open group (P<.001 in both). The cosmetic satisfaction and scar consciousness scores were the same in the robotic and endoscopic groups and were also the same in the GUA and GUAB approach groups. Patients treated by the GUA approach were more satisfied with their scarless breasts than patients treated by the GUAB approach having breast scars. CONCLUSIONS: Long-term postoperative cosmesis after robotic/endoscopic thyroidectomy using GUAB/GUA approaches is significantly better than conventional open thyroidectomy. In the robotic/endoscopic group, the scarless breasts resulting from the GUA approach lead to greater satisfaction than those after the GUAB approach.
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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 | 0.001 |
| 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) | 0.001 | 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".