The Usefulness of the Thyroid Imaging Reporting and Data System in Determining Thyroid Malignancy
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: To determine the effect of a modified Thyroid Imaging and Reporting Data System (TIRADS) in predicting malignancy in surgically treated nodules. STUDY DESIGN: Retrospective review. METHODS: This study was carried out at a tertiary care center from July 2016 to July 2017. Patients were included if they had a thyroid nodule that had an ultrasound assessment with subsequent fine-needle aspiration biopsy (FNAB) as well as surgical resection. Patients were excluded if they had previous head and neck surgery. Patients were stratified into those who had a formal modified TIRADS report by the radiologist versus those who had an ultrasound report without TIRADS reporting. FNAB results were reported as per Bethesda Thyroid Cytology Criteria, and the final pathology report was nominalized as malignant or benign. RESULTS: One hundred twenty-four consecutive patients who met the inclusion criteria listed above were included within the study. Thirty one patients (25%) had a modified TIRADS report from the radiologist, whereas 93 patients (75%) did not. There was no statistical significance between the two groups in terms of: gender (P = .24), age (P = .77), FNAB results (P = .95), final surgical pathology (P = .90), or incidental findings of malignancy (P = .09). Comparative analysis showed no statistically significant difference between the two groups in terms of the concordance of FNAB and a final pathological diagnosis of malignancy (P = .91). CONCLUSIONS: Despite the known diagnostic utility of the TIRADS in relation to FNAB results and its widespread use, this study shows that the overall detection of malignancy is not statistically different in those who received a modified TIRADS report. LEVEL OF EVIDENCE: 3 Laryngoscope, 130: 2087-2091, 2020.
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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 | 0.022 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".