Molar Pregnancy–Induced Hyperthyroidism: The Importance of Early Recognition and Timely Preoperative Management
Bibliographic record
Abstract
Hyperthyroidism due to gestational trophoblastic disease (GTD) is a rare but potentially life-threatening condition. Optimal perioperative management is crucial for favorable outcomes and prevention of thyroid storm. However, scarce data exist defining the ideal approach to this complex clinical presentation. This case report describes a first-time pregnant 32-year-old woman who was found to be biochemically hyperthyroid in the context of a 10-week gestation molar pregnancy. Despite her biochemical values, the patient remained clinically asymptomatic of her thyroid disease. The Gynecology and Anesthesiology services urgently consulted Endocrinology, and empiric treatment for prevention of potential impending thyroid storm was initiated prior to operative uterine evacuation. After 2 uneventful dilation and curettages with chemotherapy and a transient prescription of antithyroid medication, the patient normalized her human chorionic gonadotropin (hCG) level and recovered to biochemical euthyroidism. Other than a pruritic rash that may have been due to propylthiouracil, the patient's hyperthyroidism improved without further complications. This case highlights the importance of recognizing the link between GTD and thyrotoxicosis to allow for timely initiation of appropriate preoperative treatment. Fortunately, the multidisciplinary approach facilitated management to prevent evolution to thyroid storm.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".