Impact of maternal prenatal psychological stress on birth weight.
Bibliographic record
Abstract
OBJECTIVE: The study aimed to evaluate the impact of prenatal maternal stress on birth weight using a large cohort of predominantly White women living in an urban area. METHOD: Women were recruited between 2005 and 2010. Data collection took place between the 24th and the 28th week of gestation. The Measure of Psychological Stress (MSP-9), a validated tool to assess stress symptoms, was used to collect data on prenatal maternal stress (independent variable). Birth weight (dependent variable) was classified as low birth weight (<2,500 g), normal birth weight (2,500-4,000 g), and macrosomia (>4,000 g). Adjusted odds ratios (aOR) were obtained after performing multivariate logistic regressions adjusted for potential cofounders. At the final stage, 5,721 women were included in analysis. RESULTS: When compared with women experiencing low stress, participants with high stress scores were at increased risk of delivering a newborn with low birth weight before adjustment (OR = 2.06, 95% CI [1.04, 4.09]), but after adjustment, only a nonsignificant trend remained. However, women experiencing intermediate and high levels of stress were at increased risk of delivering a newborn with macrosomia, even after adjustment (aOR = 1.23, [1.02, 1.49]) and (aOR = 1.76, [1.11, 2.77]) compared to those who scored low on the psychological stress scale. CONCLUSION: Women exposed to high psychological stress during the second trimester (24th to 28th weeks) of pregnancy have a 1.8-fold increased risk for delivering a newborn with macrosomia when compared to women exposed to low psychological stress. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.003 |
| 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 | 0.000 |
| 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".