Association between race/skin color and premature birth: a systematic review with meta-analysis
Bibliographic record
Abstract
ABSTRACT OBJECTIVE To analyze the association between race/skin color and the occurrence of prematurity. METHODS Meta-analysis with observational studies, selected by a systematic review in the bibliographic databases Medline and Biblioteca Virtual da Saúde with the descriptors: “Race or ethnic group” and “ethnicity and health” associated with the words “infant premature” and “obstetric labor premature”. Articles published in the period from 2010 to 2014, of the observational epidemiological type, in Portuguese, English and Spanish, were included. Articles that did not have abstracts or that were review articles, theses, dissertations, and editorials were excluded. We adopted the relative risk and their respective confidence intervals (95%CI) as measures of effect, obtained through the random effect model and represented by the forest plot type graph. The Egger test and the Newcastle-Ottawa scale, respectively, were used to analyze possible publication biases and the quality of the studies. RESULTS Of the 926 articles identified, 17 were eligible for the study. Of the 17 full texts published, seven were retrospective cohort studies, nine were cross-sectional studies, and one was a case-control study. Except for one study, the others reported a positive association between race/color of skin and prematurity. Compared with full-term newborns, the relative risk of the combined effect in those born preterm was 1.51 (95%CI 1.39-1.69). The funnel chart suggested publication bias. CONCLUSIONS The present meta-analysis indicated a positive association for the risk of prematurity according to race/skin color.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.039 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".