Diagnosis and Treatment of an Ovarian Artery Aneurysm Rupture: A Case Report and Literature Review
Bibliographic record
Abstract
We present a rare case of a 31-year-old female, gravida 7, para 3, who presented to the emergency department 2 days following an uncomplicated vaginal delivery with complaints of abdominal pain radiating to the left flank. Multiphasic computed tomography and conventional angiography subsequently confirmed the rupture of a left ovarian artery aneurysm. The patient was treated with transcatheter arterial embolization (TAE) using microcoils and was discharged from the hospital after 2 days. A review of the English literature revealed only 44 other documented cases of ovarian artery aneurysm ruptures, with most of these cases being associated with pregnancy. Transcatheter arterial embolization has emerged as the preferred treatment for pelvic hemorrhage and is as effective as surgery, while also reducing patient complications. Spontaneous rupture of an ovarian artery aneurysm should be considered in the differential diagnosis of multiparous postpartum women presenting clinically with hypotension or shock in the absence of another identifiable source of bleeding.Clinical ImpactThis article provides a detailed review of the pathophysiology underlying the formation and rupture of ovarian artery aneurysms, their clinical presentation, and the available treatment options, with the goal of helping physicians identify and manage this rare condition.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".