Ectopic cervical thymus in children: Clinical and radiographic features
Bibliographic record
Abstract
Objectives Ectopic thymus is rare and can be a diagnostic challenge. This study evaluated the management of children radiographically diagnosed with ectopic cervical thymus. Methods A retrospective review of 100 patients was performed. Data related to clinical presentation, radiological imaging, pathology, and management were collected. Changes in lesion volume were tracked over time. Clinical characteristics were compared based on lesion location in the neck using analysis of variance modelling. Results There were 115 lesions with radiographic features of ectopic cervical thymus (15 children had bilateral lesions). Diagnosis was based on ultrasound in 98% of patients, magnetic resonance imaging in 18%, and computed tomography in 11%. Mean (SD) follow‐up duration was 2 (2.2) years. Forty‐four percent (51/115) of lesions involved the thyroid gland, 29% (33/115) were in the central neck but separate from the thyroid, 18% (21/115) had mediastinal extension, and 8% (9/115) involved the submandibular region. Location was unclear for two patients. Submandibular lesions were on average 12.4 cm 3 larger (95% CI, 8.2, 16.6) than mediastinal lesions at diagnosis, P ≤ .001. Volume of thymic tissue decreased over time, from a mean (standard deviation [SD]) volume of 4.3 cm 3 (9.2) at initial ultrasound to 2.7 cm 3 (6.1) at final ultrasound (paired t‐test, P = .008). Only two patients required surgery: one for compressive symptoms, and the other to rule out malignancy. Conclusion Ninety‐eight percent of children with ectopic cervical thymus were managed conservatively without issues. We propose a classification system based on location to ease communication among clinicians and to help follow these lesions over time. Level of Evidence 4, case series Laryngoscope, 130:1577–1582, 2020
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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 | 0.007 |
| 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 | 0.000 | 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".