Innovative imaging techniques to guide the prenatal and postnatal management of premature omphalopagus conjoined twins: a case report
Bibliographic record
Abstract
The incidence of conjoined twinning is approximately 1:100,000 with omphalopagus anatomy representing 10% of all cases. With an overall survival rate of <10% and a 60% rate of survival in patients who undergo separation, considerable medical, surgical, and ethical planning is required. A 34-year-old mother was identified as having conjoined twins at 9 weeks gestation. She underwent an early fetal Magnetic Resonance Imaging (MRI) to assess potential for viability and a late second trimester MRI – these identified omphalopagus anatomy with shared liver and a Tetralogy sequence in twin B. 3D modeling of these images guided delivery and separation planning. The twins were born prematurely at 29 weeks and 6 days. Neonatal Inteive Care Unit (NICU) management was instituted at birth and the initial cardiorespiratory status of the twins was stable with minimal circulatory mixing noted. Combined fetal weight was 2.6 kg. At 3 weeks of life, significant circulatory deterioration occurred in twin B. Urgent separation to facilitate treatment for twin B best optimized both patients’ survival and was in keeping with parental wishes. Detailed cross-sectional imaging was expedited and used to facilitate successful separation. Pre-op anesthesia time was 127 minutes and Operating Room time was 290 minutes; estimated blood loss was 150 mL. Twin B subsequently underwent emergent catheterization with pulmonary artery dilation and right ventricle outflow tract stenting. Both twins recovered successfully with twin A being discharged from hospital at 14 weeks old and twin B discharged at 15 weeks old. Cross-sectional imaging with 3D modeling helps optimize outcomes for conjoined twins.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".