OEIS complex: prenatal ultrasound and autopsy findings
Bibliographic record
Abstract
OBJECTIVE: To describe prenatal ultrasound and autopsy findings in fetuses with OEIS (omphalocele, bladder exstrophy, imperforate anus, spina bifida) complex. METHODS: This was a retrospective study of the nine cases with OEIS complex diagnosed at our center using detailed fetal ultrasound during the last 10 years. We summarized the fetal ultrasound findings that led to the diagnosis and compared them with the autopsy results. RESULTS: All affected fetuses were diagnosed using detailed fetal ultrasound after 16 weeks' gestation. The main prenatal findings were omphalocele, skin-covered lumbosacral neural tube defect, non-visualized bladder and limb defects. Prenatal sonography failed to detect the abnormal genitalia, bladder exstrophy and anal atresia. All cases had abnormalities in a 'diaper distribution', which helped in making the prenatal diagnosis. Eight of the nine couples chose to terminate the pregnancies following multidisciplinary counseling. The pregnancy that was continued was a case with dizygotic twins discordant for OEIS, and the affected fetus died in utero. CONCLUSIONS: The combination of the following ultrasound findings: ventral wall defect, spinal defect and a non-visualized bladder with or without limb defects, are characteristic of OEIS complex. Diagnosis can be made with confidence as early as 16 weeks' gestation, although earlier diagnosis may be possible.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".