Ultrasound to differentiate thyroglossal duct cysts and dermoid cysts in children
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: To determine if ultrasound could differentiate between thyroglossal duct cysts (TGDC) and midline dermoid cysts (DC). STUDY DESIGN: Cohort study. METHODS: A search of pathology reports yielded 91 patients with TGDC or midline DC. Ultrasound images were presented to a radiologist blinded to pathology who evaluated the following: 1) depth of lesion from skin, 2) maximum diameter, 3) dimensions, 4) midline location, 5) distance from base of tongue, 6) tract, 7) wall regularity, 8) wall thickness, 9) margin definition, 10) heterogeneity, 11) internal septae, 12) solid components, 13) intralesional Doppler flow, and 14) posterior enhancement. The predictive power of these variables was evaluated in a multiple logistic regression model. RESULTS: There were 53 TGDC and 38 DC. TGDC were significantly more likely than DC to have the following features: 1) smaller distance from base of tongue, 2) tract, 3) irregular wall, 4) ill-defined margin, 5) internal septae, 6) solid components, and 7) intralesional Doppler flow. Three clinically reliable ultrasound variables were independently able to discriminate between TGDC and DC. A predictive model was fashioned whereby each variable was scored as 0 or 1, with a total score calculated (septae + irregular wall + solid components = TGDC [or SIST] score). We propose a scoring system whereby 0 = suggestive of DC; 1 = suggestive of TGDC; and ≥2 = highly suggestive of TGDC. CONCLUSIONS: It may be possible to differentiate between TGDC and midline DC preoperatively using ultrasound.
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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) | 0.000 | 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 it