PFM.56 Fetal or Neonatal Haemangioma and Lymphangioma: Noteworthy differences in clinical significance and outcome
Bibliographic record
Abstract
The aim of this study was to review the clinical outcomes of congenital haemangiomas and lymphangiomas in our unit. Methods Retrospective cohort study. Results Between January 2004 and March 2013, over 67,000 infants delivered in Mount Sinai, of which 45 had a diagnosis of haemangioma (n = 23) or lymphangioma (n = 17). Twenty cases were antenatal soft tissue masses, the majority (n = 17) of which were lymphangiomas. These were usually large masses, involving the neck or head (n = 10), thorax (n = 6), abdomen (n = 2) or internal organs (n = 2). Two required antenatal fetal blood sampling and transfusion for anaemia secondary to the mass (one haemangioma, one suspected lymphangioma). Eighteen women delivered in our unit, at a median gestational age of 38 weeks. Of the eight fetuses with neck masses, three were delivered with EXIT and three by caesarean section (CS) with ENT in attendance. Four women delivered vaginally, two with fetal neck masses. Eight other women delivered by CS, either for obstetric reasons (n = 4) or to minimise trauma to the mass (n = 4). All infants survived to discharge, and twelve were transferred to another paediatric unit for further care. Conclusions Congenital haemangiomas and lymphangiomas are rare (0.06%). Those diagnosed postnatally were usually small haemangiomas occurring in preterm infants and not causing clinical concern. Antenatal diagnosis were more commonly lymphangiomas and were larger masses requiring multiple attendances to the Fetal Medicine Unit, further high level investigation and delivery within a tertiary level unit to allow for multidisciplinary care.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".