Thyroid Cancer In Children: A Multicenter International Study Highlighting Clinical Features And Surgical Outcomes Of Primary And Secondary Tumors
Bibliographic record
Abstract
Background: Thyroid gland malignancy is rare in pediatrics (0.7% of tumors); only 1.8% are observed in patients < 20 yrs with a higher prevalence recorded in females and adolescents. Risk factors include genetic syndromes - MEN disorders, autoimmune disease and ionizing radiation exposure. Radiotherapy is also linked with increased risk of secondary thyroid cancers. The present study describes the clinical features and surgical outcomes of primary and secondary thyroid tumors. Methods: Institutional data was collected on pediatric patients with thyroid cancer during 2000 - 2020 from 8 International Surgical Oncology centers. Statistical analysis was performed using GraphPad Prism. Results: Of 255 cases of thyroid cancer, only 13 (5.1%) were secondary tumors. Primary thyroid malignancies were more likely to be multifocal in origin (odds ratio [OR] 1.993, 95% confidence interval [CI] 0.7466-5.132, p 0.2323), had bilateral glandular location (OR 2.847, 95% CI 0.6835-12.68, p 0.2648) and proved metastatic at 1st diagnosis (OR 1.259, 95% CI 0.3267-5.696 p>0.999). Secondary tumors showed a higher incidence of disease relapse (OR 1.556, 95% CI 0.4579-5.57, p 0.4525) and surgical morbidity (OR 2.042, 95% CI 0.7917-5.221, p 0.1614) including hypoparathyroidism and recurrent laryngeal nerve injury. Overall survival (OS) was 99% at 1 year and 97% after 10 years. No EFS differences were evident with primary vs. secondary tumors (Chi square 0.7307, p 0.39026). Conclusions: This multicenter study demonstrates excellent survival for pediatric thyroid malignancy. Secondary tumors exhibit greater disease relapse (15.8% vs 10.5%) and a higher incidence of surgical related complications (36.8% vs 22.2%).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".