Predictive Factors for Malignancy in Atypiai of Undetermined Significance (AUS) Thyroid Nodules: A Comprehensive Retrospective Analysis
Bibliographic record
Abstract
This retrospective study aimed to identify predictive factors for malignancy in thyroid nodules classified as atypia or follicular lesion of undetermined significance (AUS/FLUS). The analysis included 165 patients who underwent thyroid nodule surgery at Ankara Numune Training and Research Hospital. Data on demographics, surgical procedures, ultrasonographic features, and pathology results were extracted and analyzed. The cohort consisted predominantly of women (79.39%) with a mean age of 46.68 years. Surgeries performed included total thyroidectomy (88%), total thyroidectomy with central lymph node dissection (6%), and modified radical neck dissection (3%). Malignancies, largely papillary thyroid carcinoma (PTC), were identified in 81 cases. Univariate analysis revealed significant associations between malignancy and ultrasonographic features like calcification, spiculated margins, and nuclear inclusions. Multivariate analysis pinpointed calcification as the only independent risk factor. Histopathological findings indicated heterogeneity within malignancies, noting lymphovascular and capsular invasion in PTC cases. These findings emphasize calcification as a key predictor of malignancy in AUS thyroid nodules and underscore the role of surgical intervention in this challenging diagnostic category, contributing to enhanced risk stratification and clinical decision-making for managing AUS/FLUS thyroid nodules.
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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 | 0.001 | 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".