Gray-Scale vs. Color Doppler Ultrasound in Cold Thyroid Nodules
Bibliographic record
Abstract
We intended to compare gray-scale vs. color Doppler ultrasound findings in cold thyroid nodules. Sixty-four patients with cold thyroid nodules for whom the presumptive diagnosis of malignancy (based on isotope scan study) had been made were consecutively included. They underwent gray-scale and color Doppler sonography studies. Based on histopathologic examination of surgically removed nodules, there were respectively 25 (39%) and 39 (61%) malignant and benign nodules. On color Doppler sonography, preference central hypervascularity was the most common finding in malignant nodules (17 nodules, 68%). Among benign nodules, preference perinodular hypervascularity was the most common finding (26 nodules, 66.7%). The most sensitive and specific Doppler sonography findings for malignant nodules were preference central hypervascularity (68%) and only central vascularity (97%), respectively. On gray-scale sonography, absent halo sign was the most common finding in malignant nodules (20 nodules, 80%). Among benign nodules, microcalcification was the most common finding which was reported in 12 nodules (30.7%). Hypoechogenicity was the most specific finding (76.9%) for malignant nodules. Since both gray-scale and color Doppler ultrasonography are inexpensive, non-invasive, and accessible methods to diagnose thyroid malignant cold nodules, it is recommended that these methods be applied by clinicians to assist or even substitute other invasive methods.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".