Maternal near‐miss among women with a migrant background in Germany
Bibliographic record
Abstract
OBJECTIVE: To examine the association between region of origin and severe illness bringing a mother close to death (near-miss). DESIGN: Retrospective cohort study. SETTING: Maternity units in Lower Saxony, Germany. POPULATION: 441 199 mothers of singleton newborns in 2001-2007. METHODS: Using chi-squared tests, bivariate and multivariable logistic regression we examined the association between maternal region of origin and near-miss outcomes with prospectively collected perinatal data up to seven days postpartum. MAIN OUTCOME MEASURES: Hysterectomy, hemorrhage, eclampsia and sepsis rates. RESULTS: Eclampsia was not associated with region of origin. Compared to women from Germany, women from the Middle East (OR 2.24; 95%CI 1.60-3.12) and Africa/Latin America/other countries (OR 2.17; 95%CI 1.15-4.07) had higher risks of sepsis. Women from Asia (OR 3.37; 95%CI 1.66-6.83) and from Africa/Latin America/other countries had higher risks of hysterectomy (OR 2.65; 95%CI 1.36-5.17). Compared to German women, the risk of hemorrhage was higher among women from Asia (OR 1.55; 95%CI 1.19-2.01) and lower among women from the Middle East (OR 0.66, 95%CI 0.55-0.78). Adjusting for maternal age, parity, occupation, partner status, smoking, obesity, prenatal care, chronic conditions and infertility showed no association between country of origin and risk of sepsis. CONCLUSION: Region of origin was a strong predictor for near-miss among women from the Middle East, Asia and Africa/Latin America/other countries. Confounders mostly did not explain the higher risks for maternal near-miss in these groups of origin. Clinical studies and audits are required to examine the underlying causes for these risks.
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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 | 0.000 | 0.001 |
| 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".