Management of Neonates Born to Mothers With Graves’ Disease
Bibliographic record
Abstract
Neonates born to mothers with Graves' disease are at risk for significant morbidity and mortality and need to be appropriately identified and managed. Because no consensus guidelines regarding the treatment of these newborns exist, we sought to generate a literature-based management algorithm. The suggestions include the following: (1) Base initial risk assessment on maternal thyroid stimulating hormone (TSH) receptor antibodies. If levels are negative, no specific neonatal follow-up is necessary; if unavailable or positive, regard the newborn as "at risk" for the development of hyperthyroidism. (2) Determine levels of TSH-receptor antibodies in cord blood, or as soon as possible thereafter, so that newborns with negative antibodies can be discharged from follow-up. (3) Measurement of cord TSH and fT4 levels is not indicated. (4) Perform fT4 and TSH levels at day 3 to 5 of life, repeat at day 10 to 14 of life and follow clinically until 2 to 3 months of life. (5) Use the same testing schedule in neonates born to mothers with treated or untreated Graves' disease. (6) When warranted, use methimazole (MMI) as the treatment of choice; β-blockers can be added for sympathetic hyperactivity. In refractory cases, potassium iodide may be used in conjunction with MMI. The need for treatment of asymptomatic infants with biochemical hyperthyroidism is uncertain. (7) Assess the MMI-treated newborn on a weekly basis until stable, then every 1 to 2 weeks, with a decrease of MMI (and other medications) as tolerated. MMI treatment duration is most commonly 1 to 2 months. (8) Be cognizant that central or primary hypothyroidism can occur in these newborns.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".