Prevalence of esophageal atresia among 18 international birth defects surveillance programs
Bibliographic record
Abstract
BACKGROUND: The prevalence of esophageal atresia (EA) has been shown to vary across different geographical settings. Investigation of geographical differences may provide an insight into the underlying etiology of EA. METHODS: The study population comprised infants diagnosed with EA during 1998 to 2007 from 18 of the 46 birth defects surveillance programs, members of the International Clearinghouse for Birth Defects Surveillance and Research. Total prevalence per 10,000 births for EA was defined as the total number of cases in live births, stillbirths, and elective termination of pregnancy for fetal anomaly (ETOPFA) divided by the total number of all births in the population. RESULTS: Among the participating programs, a total of 2943 cases of EA were diagnosed with an average prevalence of 2.44 (95% confidence interval [CI], 2.35-2.53) per 10,000 births, ranging between 1.77 and 3.68 per 10,000 births. Of all infants diagnosed with EA, 2761 (93.8%) were live births, 82 (2.8%) stillbirths, 89 (3.0%) ETOPFA, and 11 (0.4%) had unknown outcomes. The majority of cases (2020, 68.6%), had a reported EA with fistula, 749 (25.5%) were without fistula, and 174 (5.9%) were registered with an unspecified code. CONCLUSIONS: On average, EA affected 1 in 4099 births (95% CI, 1 in 3954-4251 births) with prevalence varying across different geographical settings, but relatively consistent over time and comparable between surveillance programs. Findings suggest that differences in the prevalence observed among programs are likely to be attributable to variability in population ethnic compositions or issues in reporting or registration procedures of EA, rather than a real risk occurrence difference. Birth Defects Research (Part A), 2012.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".