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Record W4409766219 · doi:10.1002/cncy.70016

Cytopathology of follicular and oncocytic follicular thyroid neoplasms: A Bethesda System perspective

2025· review· en· W4409766219 on OpenAlexaff
André Lametti, Fadi Brimo, Yonca Kanber, Derin Çağlar, Manon Auger

Bibliographic record

VenueCancer Cytopathology · 2025
Typereview
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineCytopathologyThyroidPathologyThyroid neoplasmThyroid carcinomaDifferential diagnosisThyroid nodulesFollicular cellThyroiditisBethesda systemSurgical pathologyCytologyInternal medicine

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.841
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.351
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations11
Published2025
Admission routes1
Has abstractyes

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