EP20.12: The potential impact of undiagnosed vasa previa on perinatal mortality in six countries with well‐developed healthcare systems
Bibliographic record
Abstract
Vasa previa (VP) refers to unprotected fetal vessels running in the membranes over the cervix. These vessels often rupture when spontaneous or artificial rupture of the membranes occurs, leading to rapid fetal exsanguination and death. Prenatal diagnosis is associated with >97% perinatal survival whereas lack of prenatal diagnosis is associated with 56% perinatal mortality. The objective of our study was to determine the potential perinatal mortality associated with undiagnosed VP in 6 Western nations (US, UK, Canada, Germany, Ireland and France). We used national statistics for data on annual number of births in these 6 countries, assuming a VP incidence of 1:1666 births and a 56% mortality in undiagnosed cases of VP. We then calculated the potential number of perinatal deaths that would occur annually in each of these 6 countries if no cases of VP diagnosed prenatally, as well as if 50% of cases of VP were to be diagnosed prenatally. Assuming 3,664,292, 625,008, 359,553, 795,500, 58,443, and 738,000 annual births in the US, UK, Canada, Germany, Ireland, and France annually, respectively, and a vasa previa incidence of 1 in 1,666 births, respectively, there would be 2,199, 375, 215, 477, 35, and 443 births affected by VP annually, respectively, in these countries. Assuming a 56% rate of perinatal mortality, if no cases of VP were diagnosed prenatally, there would be 1,231, 210, 120, 267, 20, and 248 potentially preventable perinatal deaths in these countries annually from VP. If 50% of cases were diagnosed, the annual deaths would be 615, 105, 60, 134, 10, and 124, respectively. If all cases were diagnosed prenatally, very few deaths would occur. In the absence of prenatal diagnosis of VP, a substantial number of perinatal deaths occur in otherwise healthy pregnancies that could be prevented by routine screening for VP. Since > 99% cases of VP can be diagnosed prenatally, and in these cases, survival approaches 100%, our study supports screening for VP.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".