Accuracy of Thyroid Fine-Needle Aspiration Cytology: A Cyto-Histologic Correlation Study in an Integrated Canadian Health Care Region with Centralized Pathology Service
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: The reported ROM within TBSRTC categories varies widely and depends on several factors in the clinical care pathway for thyroid nodules, including sonographic risk stratification, cytology expertise, selection criteria for surgical resection, and definitions of malignancy used. METHODS: We present 5,867 consecutive thyroid FNAC and corresponding surgical pathology in the context of a comprehensive, single-payer health care system with centralized cytology and surgical pathology services for over 1.5 million inhabitants. RESULTS: We report higher usage of ND and AUS/FLUS categories than the literature (19% vs. <10% and 15% vs. <10%, respectively). Our surgical resection rate for malignant cytology is substantially higher than the literature (94% vs. 50%, respectively). The ROM by the TBSRTC category in our cohort was similar to the literature. The overall diagnostic accuracy of thyroid FNAC was 92%, which is similar to other studies. Inclusion of incidental PMC as histologically malignant raised the ROM in the ND, benign, and AUS/FLUS categories. DISCUSSION: The diagnostic performance of thyroid FNAC in our study is similar to the reported literature. Differences in TBSRTC category usage likely arise from cytologist variability and expertise. Our higher surgical resection rate in the malignant cytology category reflects the greater capture of surgical follow-up within our healthcare region with centralized pathology and a single EMR system. Keeping in mind the method of calculation of ROM, the malignancy rate by TBSRTC is similar to previous reports.
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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 | 0.000 |
| 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