Impact of Transoral Endoscopic Vestibular Approach Thyroidectomy on Pathologic Assessment
Bibliographic record
Abstract
CONTEXT.—: Since 2016, transoral endoscopic thyroid resection with vestibular approach (TOETVA) has been increasingly performed in the United States. Although guidelines for the procedure are evolving, indeterminate and malignant preoperative cytopathologic diagnoses are not a contraindication. There are limited data related to the pathologic examination of these specimens. OBJECTIVE.—: To examine the clinicopathologic features of TOETVA specimens with particular attention to limitations of interpretation of pathologic parameters and final diagnosis. DESIGN.—: We reviewed age, sex, preoperative imaging and cytologic diagnoses, surgical pathology, and clinical follow-up data in TOETVA resections from our institution for procedures performed between March 2016 and December 2019. RESULTS.—: Fifty cases of TOETVA were identified, comprising 48 women and 2 men with a mean age of 47 years. Preoperative cytologic diagnoses were available in 47 cases and included 19 nondiagnostic/benign (Bethesda I/II), 24 follicular lesion of undetermined significance/suspicious for follicular neoplasm (Bethesda III/IV), and 4 suspicious/malignant diagnoses (Bethesda V/VI). Thirty-four cases (68%) among the surgical resection specimens showed disruption and/or fragmentation. Thirty-nine cases were negative for carcinoma, including hyperplasias and benign/indolent neoplasms. Eleven cases exhibited papillary thyroid carcinoma. Final diagnoses were reached in all disrupted/fragmented cases. In 2 cases of papillary thyroid carcinoma, tumor size, microscopic extrathyroidal extension, and margin status could not be determined. CONCLUSIONS.—: A significant proportion of TOETVA specimens are disrupted/fragmented, which can compromise information about tumors, including size, number, margin status, and microscopic extrathyroidal extension. Given that these parameters inform treatment and follow-up, this should be considered when selecting patients for TOETVA.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".