P33.09: The importance of imaging in a successful pregnancy complicated by Klippel‐Trenaunay‐Weber syndrome: case report
Bibliographic record
Abstract
Klippel-Trenaunay syndrome (KTS) is a rare congenital disease characterized by a triad of extensive cutaneous hemangiomas, venous varicosities and soft tissue or bone hypertrophy asymmetrically affecting one limb. Pregnant patients affected by KTS are at increased risk for thromboembolic events. Paradoxically, they can also suffer major haemorrhage due to rupture of venous varicosities or arteriovenous malformations (AVMs), including uterine angiomatosis. Uteri affected by angiomatosis demonstrate a spongiform appearance and contain anechoic and hypoechoic diagonal lines which represent abnormal vessels. The color Doppler confirms blood flow. In 1997, Richards et al reported a case with massive myometrial angiomatosis which was terminated at 13 weeks. We report a successful term delivery by Caesarean section of a pregnancy complicated by massive widespread uterine and cervical angiomatosis in a patient with KTS. Detail antenatal assessment is crucial to optimize pregnancy outcome and minimize risks in this rare hereditary disorder. In our case, extensive prenatal imaging was performed principally using ultrasound but also supported by Magnetic Resonance Imaging (MRI). This approach enabled the identification and mapping of areas of potential abnormal vascularisation not only within the uterus but also in the superficial tissues overlying the lumbar spine, lower abdomen, vulva and vagina. Only with this information could the relative risks of vaginal versus Caesarean delivery be judged. Moreover, a multidisciplinary approach is strongly recommended to improve overall patient care. Supporting information can be found in the online version of this abstract. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".