Role of Fine‐needle Aspiration Biopsy and Frozen Section in the Management of Papillary Thyroid Carcinoma Subtypes
Bibliographic record
Abstract
Since fine-needle aspiration biopsy (FNAB) was introduced, the value of frozen section (FS) has been questioned. This study compares FNAB and FS sensitivities among the usual form of papillary thyroid cancer (uPTC) and variants of PTC such as tall cell (tcPTC), follicular (fPTC), and Hurthle cell (HcPTC). A total of 257 patients who underwent preoperative FNAB, intraoperative FS, and thyroidectomy for PTC were, randomly selected from a database of a university teaching hospital in Toronto. There were 218 females (84.8%) and 39 males (15.2%), from 19 to 89 years of age (mean of 44 years), having uPTC (n = 212), fPTC (n = 24), HcPTC (n = 14), and tcPTC (n = 7). Data were analyzed using chi2 test. Sensitivities were calculated by division of true positives and by the sum of true positives and false negatives. True positives had to reflect a conclusive diagnosis of cancer. The FNAB sensitivities were uPTC (39.2%), fPTC (25%), HcPTC (42.9%), tcPTC (85.7%), similar to FS sensitivities (p = 0.497) for uPTC (44.3%), fPTC (16.7%), HcPTC (42.9%), and tcPTC (71.4%). Use of FS following FNAB increased sensitivities for uPTC to 56.1%, fPTC to 29.2%, and tcPTC to 100%. In addition, FS did not increase FNAB sensitivity in HcPTC. Combination FNAB plus FS failed in 43.9% of uPTC, 70.8% of fPTC, and 57.1% of HcPTC. We concluded that FNAB and FS sensitivity vary with PTC subtype and are still necessary for selection and treatment. The recognition of morphologic subtypes of PTC from the FNAB could optimize the selection of patients for intraoperative FS, enhance the preoperative assessment of prognosis, facilitate the surgical planning, and simplify the preparation of postoperative adjuvant therapy.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".