Ultrasound to differentiate thyroglossal duct cysts and dermoid cysts in children
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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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 | 0.001 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".