Graves’ disease in a five-month-old boy with an unusual treatment course
Bibliographic record
Abstract
Abstract Objectives Graves’ disease (GD) is rare in children under age five years. Antithyroid drugs are typically first-line therapy but carry the risks of agranulocytosis and liver dysfunction. Case presentation A male infant with multiple congenital anomalies, left ventricular hypertrophy, and neurologic dysfunction developed GD at five months of life. The presence of chronic hepatitis complicated medical management. Potassium iodide was effective temporarily, but urgent thyroidectomy was required at nine months of age. Postoperatively, the patient developed a thyroid function pattern consistent with impaired pituitary sensitivity to thyroid hormone (TH) that responded to the addition of liothyronine. Exome sequencing revealed a heterozygous de novo duplication of the ATAD3 gene cluster, suggesting a possible mitochondrial disorder. Conclusions This case describes the youngest child to date to be diagnosed with endogenous GD and to successfully undergo definitive treatment with thyroidectomy. An underlying defect in mitochondrial function is suspected, suggesting a potential novel pathophysiologic link to early-onset thyroid autoimmunity. Additionally, this case illustrated the development of impaired pituitary sensitivity to TH following thyrotoxicosis of postnatal onset, which may contribute to our understanding of hypothalamic-pituitary-thyroid (HPT) axis development.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".