Implementation of the Bethesda System for Reporting Thyroid Cytopathology: Observations From the 2011 Thyroid Supplemental Questionnaire of the College of American Pathologists
Bibliographic record
Abstract
CONTEXT: Although information about the Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) has been widely disseminated since its inception in 2007, the extent of its implementation and impact on daily practice has not been formally evaluated. OBJECTIVES: To assess the extent of uptake of TBSRTC across pathology laboratories and to evaluate its impact on daily practice by collating participant responses to the 2011 supplemental thyroid questionnaire of the College of American Pathologists. DESIGN: A questionnaire was designed to gather information about various aspects of TBSRTC and mailed in June 2011 to 2063 laboratories participating in the College of American Pathologists cytopathology interlaboratory comparison program. The participating laboratories' answers were collated and summarized. RESULTS: Seven hundred and seventy-seven laboratories (37.6%) returned the survey. Although 60.9% (n = 451) and 17.1% (n = 127) of laboratories reported using TBSRTC or planning to use it in the near future, respectively, 22% (n = 163) had no plans to implement TBSRTC. Of the latter, 32% (n = 70) stated that they were unaware of this classification system. The majority (78.3%, n = 343) of the laboratories used TBSRTC as published in the Thyroid Bethesda System atlas, whereas 21.7% (n = 95) used it with minor modifications. Most reported that the use of TBSRTC had caused either no change (n = 67, 15.2%) or only minor changes (n = 353, 80.2%) in the terminology and diagnostic criteria previously used in their laboratories. CONCLUSIONS: According to the collected data, TBSRTC is generally well implemented in pathology laboratories. However, because approximately a third of those not using this terminology are not aware of it, additional educational efforts regarding TBSRTC are warranted.
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 | 0.017 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".