The potential impact of universal screening for vasa previa in the prevention of stillbirths
Bibliographic record
Abstract
OBJECTIVES: To estimate the number of pregnancies complicated by vasa previa annually in nine developed countries, and the potential preventable stillbirths associated with undiagnosed cases. We also assessed the potential impact of universal screening for vasa previa on reducing stillbirth rates. METHODS: We utilized nationally-reported birth and stillbirth data from public databases in the United States, United Kingdom, Canada, Germany, Ireland, Greece, Sweden, Portugal, and Australia. Using the annual number of births and the number and rate of stillbirths in each country, and the published incidence of vasa previa and stillbirth rates associated with the condition, we estimated the expected annual number of cases of vasa previa, those that would result in a livebirth, and the potential preventable stillbirths with and without prenatal diagnosis. RESULTS: There were 6,099,118 total annual births with 32,550 stillbirths, corresponding to a summary stillbirth rate of 5.34 per 1,000 pregnancies. The total expected vasa previa cases was estimated to be 5,007 (95 % CI: 3,208-7,201). The estimated number of livebirths would be 4,937 (95 % CI: 3,163-7,100) and 3,610 (95 % CI: 2,313-5,192) in pregnancies with and without a prenatal diagnosis of VP. This implies that prenatal diagnosis would potentially prevent 1,327 (95 % CI: 850-1,908) stillbirths in these countries, corresponding to a potential reduction in stillbirth rate by 4.72 % (95 % CI: 3.80-5.74) if routine screening for vasa previa was performed. CONCLUSIONS: Our study highlights the importance of universal screening for vasa previa and suggests that prenatal diagnosis of prevention could potentially reduce 4-5 % of stillbirths.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".