Management of Prenatally Diagnosed Abdominal Lymphatic Malformations
Bibliographic record
Abstract
INTRODUCTION: Abdominal lymphatic malformations (ALM) are rare congenital malformations that can regress spontaneously or lead to serious complications. Thus, the appropriate management may be challenging, particularly since pertinent literature is missing. We present our experience in the management of 5 patients with prenatally diagnosed ALM and their outcome and propose a decision-making algorithm. MATERIAL AND METHODS: We retrospectively reviewed the history, diagnostics, therapy, complications, and outcome of 5 patients with a prenatal diagnosis of ALM, referred to our department between January 2006 and February 2008. RESULTS: ALM was prenatally diagnosed by ultrasound in all patients (gestational age 21, 23, 23, 32, and 34 weeks). MRI was performed pre- and postnatally in one patient and postnatally in another. Clinical symptoms ranged from none to respiratory distress and abdominal compartment syndrome. One ALM involuted. 2 patients underwent primary OK-432 treatment. This led to a 70% size reduction in one patient. The other developed massive intracystic bleeding and required emergency surgery. 2/3 patients with surgery needed segmental bowel resection and 3/3 stayed recurrence-free. Complications included one partial inferior vena cava thrombosis after surgery, one subileus, and one hemorrhage after OK-432 application. CONCLUSION: Asymptomatic and regressing ALM are best managed conservatively ("watchful waiting") while symptomatic ALMs require surgery. Further studies are necessary to determine the ideal timepoint for intervention for non-regressing ALM.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".