Caucasian and Asian difference in role of type 1 diabetes on large-for-gestational-age neonates
Bibliographic record
Abstract
INTRODUCTION: Racial differences in the association between type 1 diabetes mellitus (T1DM) and large-for-gestational-age (LGA) neonates remain unclear. The objective of this study was to compare the effect of T1DM on LGA neonates between Caucasian and Asian women. RESEARCH DESIGN AND METHODS: A population-based retrospective cohort study was conducted among Caucasian and Asian women who had prenatal screening and gave a singleton live birth in an Ontario hospital between April 2015 and March 2018. Multivariable log-binomial regression models were used to estimate the adjusted relative risks (aRRs) and 95% CIs of T1DM on LGA for Caucasian and Asian women. Relative contribution of T1DM to LGA was examined by multivariable logistic regression model, stratified by Caucasian and Asian women. RESULTS: A total of 232 503 women (69.4% Caucasians and 30.6% Asians) were included in the final analysis. The rate of T1DM was higher in Caucasians (0.5%) than in Asians (0.2%), and the rate of LGA neonates was also higher in Caucasians (11.0%) than in Asians (5.0%). The association between T1DM and LGA in Caucasians (aRR 4.18, 95% CI (3.84 to 4.55)) was more robust than that in Asians (aRR 2.11, 95% CI (1.24 to 3.59)). T1DM was the fourth strongest contributor to LGA in Caucasians, while T1DM was the seventh contributor to LGA in Asians. CONCLUSIONS: T1DM plays a more substantial role in LGA among Caucasians than Asians. Clinicians should be aware of the Caucasian-Asian differences of effects of T1DM on LGA when developing pregnancy management strategies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".