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Record W4238563828 · doi:10.1159/000451020

The Bethesda System for Reporting Thyroid Cytopathology: Proposed Modifications and Updates for the Second Edition from an International Panel

2016· review· en· W4238563828 on OpenAlexaff
Marc Pusztaszeri, Esther Diana Rossi, Manon Auger, Zubair Baloch, Justin A. Bishop, Massimo Bongiovanni, Ashish Chandra, Béatrix Cochand‐Priollet, Guido Fadda, Mitsuyoshi Hirokawa, Soon Won Hong, Kennichi Kakudo, Jeffrey F. Krane, Ritu Nayar, Sareh Parangi, Fernando Schmitt, William C. Faquin

Bibliographic record

VenueActa Cytologica · 2016
Typereview
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsMedicineThyroid nodulesMedical physicsThyroidInternal medicine

Abstract

fetched live from OpenAlex

The Bethesda System for Reporting Thyroid Cytology (TBSRTC) was proposed in 2007 at the National Cancer Institute Thyroid Fine Needle Aspiration State of the Art and Science Conference held in Bethesda, Maryland. The aim was to address the inconsistent and sometimes confusing reporting terminologies used for thyroid FNA throughout the world. The TBSRTC consists of 6 diagnostic categories, each associated with an implied risk of malignancy that translates directly into a clinical management algorithm. Since the publication of the TBSRTC cytology Atlas in January 2010, considerable experience has been gained regarding its application in cytology practice, clinical impact, and limitations. In conjunction with the International Academy of Cytology (IAC), an international panel composed of sixteen cytopathologists and an endocrinologist with special interest in thyroid cytology, including several co-authors of the 2010 TBSRTC Atlas, was created to: (1) analyze the current worldwide impact of TBSRTC, (2) report on the current state of TBSRTC based upon a review of the published literature, and (3) provide possible recommendations for a future update of TBSRTC. Herein, we summarize the panel's deliberations and key recommendations that our panel hopes will be useful during the preparation of the second edition of TBSRTC.

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 imitation

Not 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.

metaresearch head score (Codex)0.049
metaresearch head score (Gemma)0.070
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.049
Threshold uncertainty score0.260

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0490.070
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0150.012
Science and technology studies0.0010.003
Scholarly communication0.0060.005
Open science0.0070.004
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0040.007

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 head0.117
GPT teacher head0.378
Teacher spread0.261 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations122
Published2016
Admission routes1
Has abstractyes

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