Can Thyroid Ultrasonography Predict Substernal Extension or Tracheal Compression in Goiters?
Bibliographic record
Abstract
Purpose To determine whether an ultrasonography (US)-defined thyroid volume can accurately predict substernal extension or tracheal narrowing. Methods After research ethics approval, we identified patients with thyroid nodules investigated with both US and computed tomography (CT). Reviewers assigned scores for both substernal extension and tracheal compression on CT using pre-established classification systems. Statistical analysis with receiver operating characteristic curve analysis was performed to find the US-determined thyroid volume thresholds that correlated with each substernal extension and tracheal compression. Results This study included 120 patients (mean age 63.4 years; SD ± 15.9; 67% female). Thirty-five patients (29%) had substernal extension. The mean US total thyroid gland volume in patients with and without substernal extension were 92.4 and 37.6 cm 3 , respectively ( P < .001). 86% of patients with substernal extension had tracheal narrowing vs. 27% of patients without substernal extension ( P < .0001). A cutoff dominant gland volume of ≥37.5 cm 3 showed 83% sensitivity and 79% specificity for substernal extension (area under the curve [AUC] = 0.84). A total thyroid gland volume threshold of ≥37.8 cm 3 showed 89% sensitivity and 87% specificity for any degree of tracheal narrowing (AUC = 0.90). Conclusions This study suggests that US volumes may be used as a predictor to identify those patients with thyroid enlargement who are most at risk of substernal extension and tracheal compression and who may benefit from preoperative CT imaging for optimal surgical and anesthetic planning.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".