Placenta previa: Its relationship with race and the country of origin among Asian women
Bibliographic record
Abstract
OBJECTIVES: To examine the association between placenta previa with maternal race and its variations by country of origin among Asian women. STUDY DESIGN: Retrospective cohort study. METHODS: We analyzed data from a population-based retrospective cohort study of 16,751,627 pregnancies in the US. The data were derived from the national linked birth/infant mortality database for the period 1995-2000. Multiple logistic regressions were used to describe the relationship between placenta previa and race as well as country of origin among Asian women. RESULTS: About 3.3 per 1,000 pregnancies were complicated with placenta previa among white women, while the corresponding figures for black women and women of other races were 3.0 and 4.5 per 1,000 pregnancies, respectively. The excess risk remained substantial and significant after adjustment for confounders for women of other races compared to white women. The frequencies of placenta previa among Chinese, Japanese, Filipino, Asian Indian, Korean, Vietnamese and other Asian or Pacific Islander were 5.6, 5.1, 7.6, 4.5, 5.9, 4.4 and 4.4 per 1,000 pregnancies, respectively. The adjusted odds ratios ranged from 1.39 to 2.15 among Asian women by country of origin, with the lowest for Japanese and Vietnamese and the highest for Filipino women in our study. CONCLUSION: Asian women have excess risk of placenta previa compared with white women. Major variation exists in placenta previa risk among Asian women, with the lowest risk in Japanese and Vietnamese women and the highest risk in Filipino women.
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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 | 0.001 | 0.001 |
| 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".