The Bethesda System for Reporting Thyroid Cytopathology: Proposed Modifications and Updates for the Second Edition from an International Panel
Bibliographic record
Abstract
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.
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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 | 0.049 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.007 |
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.
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".