Incidence and Predictors of Mortality in Gastroschisis: A Population-Based Study of 4,803 Cases in the United States [35K]
Bibliographic record
Abstract
INTRODUCTION: Gastroschisis is a rare congenital anomaly consisting of a full-thickness abdominal wall defect, with survival normally exceeding 90%. Few large-scale studies have examined the predictors of mortality for these infants. Our objective was to use a large database to determine predictors of mortality in gastroschisis, its incidence and mortality rate over time. METHODS: Using the “Period linked Birth-Infant Death” database (CDC) from 2009 to 2013, we collected all infants coded as having gastroschisis. Using multivariable linear regression for various maternal and infant characteristics, we calculated predictors of mortality. We also determined the incidence, death rate and age of death for our cohort. RESULTS: There were 4,803 cases of gastroschisis, with 287 deaths. The incidence of gastroschisis increased from 2.04 to 2.49/10,000 births. The rate of death stayed constant around 5.9%. 38.1% of these infants died on day 0 of life. Statistically significant predictors of mortality were paying out of pocket for healthcare, maternal obesity, pre-pregnancy diabetes, birth weight <2,500 g, gestational age <34 weeks and the presence of an additional congenital anomaly (46.2% mortality, OR 13.91). CONCLUSION: The incidence of gastroschisis in the United States increased, yet mortality rate remained stable. Mothers with DM1 or 2 should be counselled on the significantly higher risk of mortality, and be encouraged to diligently control their glycemias. The infants born preterm <34 weeks, or with birth weights <2,500 g were at the highest risk. The presence of an additional congenital anomaly is an important prognostic factor, with a high associated mortality. This information can be used in prenatal counselling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".