Neonatal Graves’ disease from high maternal TRAB antibody levels despite definitive therapy
Bibliographic record
Abstract
Graves’ disease is an autoimmune thyroid disorder that poses significant risks during pregnancy, affecting both the mother and fetus. We present four cases of neonatal Graves’ disease arising in the offspring of two women treated for Graves’ disease, highlighting the disease's transient but potentially severe impact on newborns. In the first case, the pregnant woman underwent a total thyroidectomy during the second trimester, whereas in the second case, the patient had Graves’ disease and was treated with radioactive iodine six years prior. Both women demonstrated a very high level of thyrotropin receptor antibodies (TRAb) during pregnancy. These cases underline the necessity of vigilant monitoring of TRAb levels in pregnant women with a history of Graves’ disease, regardless of previous treatment or current euthyroid status. This approach aims to facilitate early detection and treatment of neonatal Graves’ disease, mitigating its impact on growth and development. Our findings support recommendations for baseline and mid-gestation TRAb testing in this population, as well as immediate postnatal testing for neonates born to mothers with active or inactive Graves’ disease. Teaching Points: • Elevated TRAb at 20-24 weeks needs a multidisciplinary team for optimal care. • Thyroidectomy/RAI does not eliminate neonatal GD risk if maternal TRAb remains high. • GD pregnancies, even if euthyroid, require careful TRAb level monitoring. • Low maternal TRAb levels lower neonatal GD risk in pregnancy with GD history. • Early NG detection and care are crucial to support growth and development. Clinical Relevance: This paper highlights the importance of TRAb monitoring in pregnant patients with GD post-definitive therapy, even when in euthyroid state. Monitoring can help early detection and management of neonatal GD and improve outcomes. Increased awareness among healthcare providers enhances comprehensive maternal and neonatal management in the setting of maternal history of GD.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".