Cytologic grading of primary malignant salivary gland tumors: A blinded review by an international panel
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Fine needle aspiration (FNA) is commonly used for the preoperative evaluation of salivary gland tumors. Tumor grade is a key factor influencing clinical management of salivary gland carcinomas (SGCs). To assess the ability to grade nonbasaloid SGCs in FNA specimens, an international panel of cytopathologists convened to review and score SGC cases. METHODS: The study cohort included 61 cases of primary SGC from the pathology archives of 3 tertiary medical centers. Cases from 2005 to 2016 were selected, scanned, and digitized. Nineteen cytopathologists blinded to the histologic diagnosis reviewed the digitized cytology slides and graded them as low, high, or indeterminate. The panelists' results were then compared to the tumor grades based on histopathologic examination of the corresponding resection specimens. RESULTS: All but 2 of the 19 (89.5%) expert panelists review more than 20 salivary gland FNAs per year; 16 (84.2%) of the panelists work at academic medical centers, and 13 (68.4%) have more than 10 years' experience. Participants had an overall accuracy of 89.4% in the grading of SGC cases, with 90.2% and 88.3% for low- and high-grade SGC, respectively. Acinic cell carcinoma and mucoepidermoid carcinoma had the highest degree of accuracy, while epithelial-myoepithelial carcinoma and salivary duct carcinoma had the lowest degree of accuracy. As expected, the intermediate-grade SGC cases showed the greatest variability (high-grade, 42.1%; low-grade, 37.5%, indeterminate, 20.4%). CONCLUSION: This study confirms the high accuracy of cytomorphologic grading of primary SGC by FNA as low- or high-grade. However, caution should be exercised when a grade cannot be confidently assigned.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 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) | 0.000 | 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 it