Cytopathology of follicular and oncocytic follicular thyroid neoplasms: A Bethesda System perspective
Bibliographic record
Abstract
The third edition of The Bethesda System for Reporting Thyroid Cytopathology includes category IV, follicular neoplasm (FN), which is used to classify fine-needle aspirates of thyroid nodules that may correspond to invasive follicular-derived neoplasia other than papillary thyroid carcinoma. This diagnosis is infrequently rendered, and may represent a challenge for pathologists. This review presents a practical approach to FN and its subtype oncocytic follicular neoplasm (OFN). First, minimal criteria for the diagnosis must be achieved, namely sufficient cellularity, architectural features consistent with neoplasia, and follicular cell or oncocytic cytomorphology. Second, select diagnoses that are common or important differential diagnoses for FN or OFN must be ruled out, via a combination of morphological findings and limited ancillary tests, when available. These include follicular nodular disease, parathyroid sampling, metastatic carcinoma, noninvasive follicular thyroid neoplasm with papillary-like nuclear features, medullary thyroid carcinoma, certain subtypes of papillary thyroid carcinoma, and lymphocytic thyroiditis. This approach should allow for a careful selection of cases where diagnostic thyroid lobectomy is an appropriate therapeutic modality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".