Interleukin-10 Predicts Preterm Birth in Acculturated Hispanics
Bibliographic record
Abstract
OBJECTIVE: Among Hispanics living in the United States, acculturation is associated with an increased risk for preterm birth. Inflammatory pathways are also associated with preterm birth. As such, the current study sought to investigate the potential relationships among preterm birth, acculturation of Hispanic women, and inflammatory markers. STUDY DESIGN: The authors performed an observational study on pregnant Hispanic women in Texas at 22-24 weeks' gestation (n = 470). The authors obtained demographic data prenatally as well as birth outcome data from the medical chart after delivery. The authors obtained venous blood and used plasma to assay interleukin-1 receptor antagonist (IL-1RA), interleukin-6 (IL-6), and interleukin-10 (IL-10). The authors used logistic regression to understand whether the presence or the absence of IL-10 levels was related to acculturation and the risk of preterm birth. RESULTS: The authors observed interactions between undetectable IL-10 levels and years in the United States and undetectable IL-10 levels and being born in the United States in models predicting preterm birth. Follow-up probes of these interactions suggested that when IL-10 was undetectable, preterm birth became more likely as time living in the United States increased, χ(2) = 5.15 (1, 416), p = .020, odds ratio (OR) = 3.17, and was more likely in participants born in the United States than in those born elsewhere, χ(2) = 5.35 (1, 462), p = .020, OR = 16.78. The authors observed no interactions among acculturation, preterm birth, and IL-1RA and IL-6 levels. CONCLUSION: Acculturated Hispanics who lack the protective effects of IL-10 experience a markedly higher risk of preterm birth than nonacculturated Hispanics.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".