Value Of Anteroposterior-To-Transverse Ratio in Ultrasonic Diagnosis of Thyroid Nodule in Different Locations
Bibliographic record
Abstract
PURPOSE: To investigate the value of anteroposterior-to-transverse ratio (ATR) and the effect on features of nodules in ultrasound (US) diagnosis of thyroid nodules in different locations. Methods: The nodules were divided into three groups according to the different nodule location: isthmus group; upper and lower poles of bilobed thyroid group; and the middle of the bilobed thyroid group. The diameters of the nodules were recorded, and ATR of the nodule was calculated on the transverse and longitudinal sections. The transverse and the longitudinal sections of ATR of thyroid nodules in different groups were compared. Result: The transverse section of ATR was significantly different among the three groups (p = 0.001). In addition, there are significant differences in many US features among three groups, including nodule composition, thyroid parenchyma, morphology, echogenicity, shape, calcifications, vascularity, nodule ACR TI-RADS and histopathologic (all p < 0.05). In the group of upper and lower poles of bilobed thyroid, significant difference was found between the transverse and the longitudinal section of ATR (p = 0.019). The cut-off values of transverse section and longitudinal section of ATR were 0.967 and 0.750, respectively. Conclusion: The transverse section of ATR at different location of thyroid may be a predictor for malignancy with clinical diagnostic significance.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".