Effects of the September 11, 2001 disaster on pregnancy outcomes: a systematic review
Bibliographic record
Abstract
BACKGROUND: The terrorist explosions of the World Trade Center in New York City and the other events on the Pentagon and in Pennsylvania on 11 September 2001 were stressful events that affected people around the world. Pregnant women and their offspring are especially vulnerable during and after such a terrorist attack. The objective was to systematically review the risks of adverse pregnancy outcomes after the terrorist attacks on Sept 11, 2001. METHODS: The Meta-analysis of Observational Studies in Epidemiology (MOOSE) criteria were used for reporting of this review. Statistical analyses were performed using RevMan 5.0. RESULTS: Ten reports of low-to-moderate risk of methodological bias were included. There was increased risks of infants with birthweight of 1,500 g-1,999 g (adjusted odds ratio [AOR] 1.67 [95%CI 1.11-2.52]) and small-for-gestational age births (AOR 1.90; 95%CI 1.05-3.46) in New York. There was increased risks of low birthweight (relative risk 2.25; 95%CI 1.29-3.90) and preterm births (relative risk 1.50; 95%CI 1.06-2.14) among ethnically Arabic women living in California There was a reduction in birthweight by 276 g and in head circumference by 1 cm when DNA adducts, a marker for environmental toxin exposure, were doubled in maternal blood. In Holland, a 48-g reduction in birthweight was reported. CONCLUSIONS: The World Trade Center disaster influenced pregnancy outcomes in New York, among ethnically Arab women living in California and among Dutch women. The adverse outcomes are likely due to environmental pollution and stress in New York, ethnic harassment in California and communal bereavement and stress in Holland.
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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 | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".