Long-term Risk for Type 1 Diabetes and Obesity in Early Term Born Offspring: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
CONTEXT: Prematurity increases the long-term risks for endocrine and metabolic morbidity of offspring, but there is uncertainty regarding the risks for early-term deliveries (370/7-386/7 weeks of gestation). OBJECTIVE: We aim to evaluate whether early-term deliveries increase the long-term risk for type 1 diabetes and obesity of offspring up to the age of 18 years compared with full-term children. PubMed, Medline, and EMBASE were searched. Observational cohort studies addressing the association between early-term delivery and long-term risk for type 1 diabetes and obesity, were included. Two independent reviewers extracted data and assessed risk of bias. Pooled relative risks (RRs) and heterogeneity were determined. Publication bias was assessed by funnel plots with Egger's regression line and contours, and sensitivity analyses were performed. RESULTS: Eleven studies were included following a screen of 7500 abstracts. All studies were scored as high quality according to the Newcastle-Ottawa Quality Assessment Scale. Early-term delivery was significantly associated with an increased risk for type 1 diabetes (RR 1.19, 1.13-1.25), while the association was weaker for overweight and obesity (RR 1.05, 0.97-1.12). It is challenging to determine whether the association between early-term births and long-term morbidity represents a cause and effect relationship or is attributable to confounders. Most of the included studies adjusted for at least some possible confounders. CONCLUSION: Compared with full-term offspring, early-term delivery poses a modest risk for long-term pediatric type 1 diabetes. Our analysis supports that, whenever medically possible, elective delivery should be avoided before 39 completed weeks of gestation.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.029 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".