Prevalence and predictors of mortality in gastroschisis: a population-based study of 4803 cases in the USA
Bibliographic record
Abstract
Purpose: Gastroschisis is a rare congenital anomaly consisting of an abdominal wall defect resulting in extrusion of the abnormal organs. Survival of these infants exceeds 90%. Few large-scale studies have examined the predictors of mortality for these infants. Our objective was to conduct a population-based study to determine prevalence and predictors of mortality among infants born with gastroschisis.Materials and methods: We used the “Period Linked Birth-Infant Death” database to create a cohort of all births occurring between 2009 and 2013. Infants were categorized by the presence of gastroschisis, excluding infants born at <24-week gestation. Baseline maternal and newborn characteristics were compared for infants who survived and those who died. Multivariate logistic regression models were used to estimate the effect of maternal and fetal factors on mortality, while adjusting for appropriate baseline characteristics.Results: There were 4803 cases of gastroschisis, with 287 deaths. The prevalence of gastroschisis increased from 2.04 to 2.49/10,000 births over the study period. The rate of death stayed constant at about 5.9%. We found that 38.1% of these infants died on day 0 of life. Statistically significant predictors of mortality were the presence of an additional congenital anomaly, birth weight <2500 g, prepregnancy diabetes, gestational age <34 weeks, paying out of pocket for healthcare, and maternal obesity.Conclusions: The prevalence of gastroschisis in the USA increased, yet the mortality rate remained stable. Infants born preterm <34 weeks, with birth weights <2500 g, or with an additional congenital anomaly were at the highest risk of death.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".