The 2022 WHO classification of thyroid tumors: novel concepts in nomenclature and grading
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The fifth edition of the Classification of Endocrine and Neuroendocrine Tumors has been released by the World Health Organization. This timely publication integrates several changes to the nomenclature of non-neoplastic and neoplastic thyroid diseases, as well as novel concepts that are essential for patient management. The heterogeneous group of non-neoplastic and benign neoplastic lesions are now collectively termed as 'thyroid follicular nodular disease' to better reflect the clonal and non-clonal proliferations that clinically present as multinodular goiter. Thyroid neoplasms originating from follicular cells are distinctly divided into benign, low-risk and malignant neoplasms. The new classification scheme stresses that papillary thyroid carcinoma (PTC) should be subtyped based on histomorphologic features irrespective of tumor size to avoid treating all sub-centimeter/small lesions as low-risk disease. Formerly known as the cribriform-morular variant of PTC is redefined as cribriform-morular thyroid carcinoma since this tumor is now considered a distinct malignant thyroid neoplasm of uncertain histogenesis. The 'differentiated high-grade thyroid carcinoma' is a new diagnostic category including PTCs, follicular thyroid carcinomas and oncocytic carcinomas with high-grade features associated with poorer prognosis similar to the traditionally defined poorly differentiated thyroid carcinoma as per Turin criteria. In addition, squamous cell carcinoma of the thyroid is now considered a morphologic pattern/subtype of anaplastic thyroid carcinoma. In this review, we will highlight the key changes in the newly devised fifth edition of the WHO classification scheme of thyroid tumors with reflections on its applicability in patient management and future directions in this field.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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