Fetal Ovarian Cysts in Prenatal Imaging: Diagnostic Challenges and Management Options
Bibliographic record
Abstract
Fetal ovarian cysts (FOCs) are a rare prenatal finding that may be associated with maternal, fetal, or neonatal complications. They are classified by various features - small or large, simple or complex, unilateral or bilateral - which determine whether active treatment or simple observation is required. Prenatal ultrasound enables diagnosis as early as the first trimester, though most cases are detected in the second or third trimester. We present a case of a simple, small FOC diagnosed at 27 weeks of gestation in primigravida without accompanying diseases. The cyst remained uncomplicated throughout pregnancy and after birth, with spontaneous regression observed within the first year of life. We also conducted a brief literature review on the management of different types of FOCs. Small, asymptomatic FOCs detected in the second or third trimester usually require only ultrasound monitoring, as most regress spontaneously within the first year after birth. Symptomatic neonatal ovarian cysts, as well as those that enlarge during follow-up in pregnancy, carry a risk of ovarian torsion and generally require surgical intervention. Complex cysts and large cysts may be monitored conservatively unless they cause symptoms or show growth on serial ultrasounds.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".