Predicting malignancy of thyroid nodule using EU-TIRADS ultrasound score in an area of iodine deficiency
Bibliographic record
Abstract
Background: In iodine-deficient region, thyroid nodules (TN) are more frequent and the proportion of benign nodules is higher compared to areas with sufficient iodine intake. Objective The aim of this study was to assess factors associated to malignant TN specially the Eu-TIRADS ultrasound score in a Moroccan population with high prevalence of iodine deficiency. Methods: In a prospective cross-sectional study, we enrolled outpatients referred for TN with Fine Needle Aspiration Biopsy (FNAB) indication according to EU-TIRADS 2017 classification. Malignancy of TN was defined by histological results indicating a thyroid malignancy on thyroid micro-biopsy or after surgical resection if indicated. Results: 225 TN were enrolled. The median of the patients’ age was 54 (42.5 ; 62) years-old. The prevalence of malignant TN was 8.9%. Malignant TN were significantly smaller than non-malignant ones (p = 0.008) and sub-centimetric TN were more frequent in malignant TN (25% vs 1.5%; p < 0.001). On multivariate analysis, male gender (OR : 9.33 ; 95% CI [2.02 ; 43.01]; p = 0,004) and EU-TIRADS 5 score (OR : 55.6 ; 95% CI [9.34 ; 327.91] ; p < 0.001) were independent factors associated with malignant TN. The EU-TIRADS 4 score was not associated to malignant TN. Conclusion Our data suggest a trend of over-diagnosing indolent thyroid cancer. The EU-TIRADS 4 score was not associated to TN malignancy. Considering clinical, accessory sonographic features and size threshold for FNAB in EU-TIRADS 4 score TN may refine the diagnosis performance of this score category in our population.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".