Transoral endoscopic thyroidectomy vestibular approach for papillary thyroid microcarcinoma: an analysis of clinical outcomes.
Bibliographic record
Abstract
OBJECTIVE: To analyze the influence of transoral endoscopic thyroidectomy vestibular approach (TOETVA) on the clinical outcomes of patients with papillary thyroid microcarcinoma (PTMC). METHODS: The clinical data of PTMC patients (n=90) who visited the Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University from July 2019 to July 2021 were retrospectively analyzed. Patients who underwent endoscopic thyroidectomy via the transthoracic-areola approach were included in the control group (CG; n=42) and those with TOETVA were assigned to the observation group (OG; n=48). The operative time (OT), length of hospital stay (LOS), postoperative drainage volume, and complications were recorded. Besides, C-reactive protein (CRP), white blood cell count (WBC), erythrocyte sedimentation rate (ESR), as well as scores of the Visual Analogue Scale (VAS), Vancouver Scar Scale (VSS), postoperative patient satisfaction, and the Short-Form 36 Item Health Survey (SF-36) were compared between the two groups. RESULTS: The data showed that the OT and LOS of the OG were not statistically different from those of the CG, and the postoperative drainage volume was less than that of the CG (P<0.05). The two cohorts of patients showed a similar incidence of complications such as postoperative hematoma, transient hoarseness, infection, temporary recurrent laryngeal nerve injury and transient hypothyroidism (all P>0.05). CRP, WBC and ESR increased in both groups after treatment but showing no evident difference between groups. The OG had statistically lower VAS and VSS scores at two days after surgery, a statistical higher satisfaction rate than the CG, and a statistically higher score of SF-36 at three months after surgery than in the CG (all P<0.05). CONCLUSIONS: While ensuring the therapeutic effect, TOETVA can significantly reduce the pain degree of patients and scarring, as well as provide better cosmetic effect, higher patient satisfaction, and better quality of life, which is worthy of clinical promotion.
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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 | 0.001 |
| 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".