Socio‐economic disparities in pregnancy outcome: why do the poor fare so poorly?
Bibliographic record
Abstract
In this paper, we review the evidence bearing on socio-economic disparities in pregnancy outcome, focusing on aetiological factors mediating the disparities in intrauterine growth restriction (IUGR) and preterm birth. We first summarise what is known about the attributable determinants of IUGR and preterm birth, emphasising their quantitative contributions (aetiological fractions) from a public health perspective. We then review studies relating these determinants to socio-economic status and, combined with the evidence about their aetiological fractions, reach some tentative conclusions about their roles as mediators of the socio-economic disparities. Cigarette smoking during pregnancy appears to be the most important mediating factor for IUGR, with low gestational weight gain and short stature also playing substantial roles. For preterm birth, socio-economic gradients in bacterial vaginosis and cigarette smoking appear to explain some of the socio-economic disparities; psychosocial factors may prove even more important, but their aetiological links with preterm birth require further clarification. Research that identifies and quantifies the causal pathways and mechanisms whereby social disadvantage leads to higher risks of IUGR and preterm birth may eventually help to reduce current disparities and improve pregnancy outcome across the entire socio-economic spectrum.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".