The association between inter-twin birth weight discordance and hepatitis C: The United States 2011–2015 twin birth registration data
Bibliographic record
Abstract
BACKGROUND: Twins with discordant growth have increased risks of perinatal mortality and morbidity. Previous studies have identified a number of risk factors for inter-twin birth weight discordance, yet no study has examined the effect of maternal hepatitis C infection. METHODS: We used the twin birth records extracted from the 2011 to 2015 United States birth records created by the Centers for Disease Control and Prevention. The outcome variable of this study was inter-twin birth weight discordance, defined as [(birth weight of larger twin-birth weight of smaller twin) / birth weight of larger twin]. The independent association of hepatitis C infection with birth weight discordance was examined using the gamma regression or log binomial regression, adjusted by potential confounders. RESULTS: Of the 270,256 twin pairs included in the final analysis, 850 (0.31%) had positive hepatitis C. Compared to mothers without hepatitis C, mothers with hepatitis C positive tended to have higher risk of birth weight discordance, but with no statistical significance. After adjustment for potential confounding factors, hepatitis C positive became a significant risk factor for birth weight discordance >25% (relative risk 1.14, 95% confidence interval 1.02-1.29). Sensitivity analyses (by treating birth weight discordance as a continuous outcome or dichotomizing into by different cutoffs) yielded similar results, with relative risks ranging from 1.07 to 1.12 (all P<0.05). CONCLUSION: Maternal hepatitis C positive is associated with inter-twin birth weight discordance, an important adverse infant outcome in twin pregnancies, although the effect size is small.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".