Electronic Synoptic Reporting of Thyroid Nodules: Potential for Reduction in Number of Patients Undergoing Thyroid Nodule Biopsies
Bibliographic record
Abstract
Purpose: The objective of the study was to design and implement an electronic synoptic report for thyroid sonography that incorporates the thyroid imaging reporting and data system (TIRADS) and assess potential for reducing unnecessary fine needle aspiration biopsies (FNAB) of thyroid nodules. Methods: The electronic synoptic report was developed using a relational database based on elements from TIRADS and a multidisciplinary consensus statement for thyroid reporting. A retrospective analysis of 138 patients with previously reported thyroid sonographic exams was evaluated for the presence of these elements. The electronic synoptic report calculates the TIRADS score and generates a formal report. Using the TIRADS score the potential decrease in unnecessary FNAB was estimated. Results: Key TIRADS elements were variously reported ranging from 43% for the thyroid nodule’s architecture as solid or cystic. Thyroid nodule echogenicity and calcification was commented in 27% and 23%, respectively. Other features of the TIRADS score were commented in 0% to 8% of the official reports. Estimated reduction for potentially reduced need for FNAB was 34.5%. Conclusions: This study is the first implementation of synoptic reporting using a relational database for sonography of thyroid nodules. Implementation of an electronic standardized synoptic reporting system may facilitate more accurate, and more comprehensive reporting for thyroid ultrasound scanning of thyroid nodules. The use of TIRADS was estimated to be able to potentially reduce the need for FNAB which was significant.
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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 | 0.020 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".