Pitfalls in the Diagnosis of Follicular Epithelial Proliferations of the Thyroid
Bibliographic record
Abstract
The diagnosis of follicular epithelial neoplasms is an area of controversy. We provide our experience with common problems that practising pathologists face when confronted with follicular epithelial proliferations. One of the major issues is the recognition of the diagnostic nuclear features of papillary thyroid carcinoma and reactive cytologic atypia. We discuss the definitions of capsular invasion, vascular invasion, and extrathyroidal extension and their implications in cancer diagnosis and staging. We propose unified terminology for benign follicular epithelial proliferations in the setting of multinodular goiter. We also review challenges related to oncocytic change, malignant transformation in benign nodules, focal dedifferentiation, and the application of ancillary tools in thyroid pathology. We believe that this review contains comprehensive and up to date information that will be of value to pathologists who practice surgical pathology of thyroid.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".