Accuracy of Thyroid Fine-Needle Aspiration Cytology: A Cyto-Histologic Correlation Study in an Integrated Canadian Health Care Region with Centralized Pathology Service
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.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".