Risk of Severe Maternal Morbidity After Bariatric Surgery
Bibliographic record
Abstract
OBJECTIVE: We assessed whether bariatric surgery before pregnancy lowers the risk of severe maternal morbidity to a level comparable to no obesity. SUMMARY OF BACKGROUND DATA: Obesity is a risk factor for severe maternal morbidity, but the potential for bariatric surgery to reduce the risk has not been studied. METHODS: We analyzed a retrospective cohort of 2,412,075 deliveries between 1989 and 2019 in Quebec, Canada. The main exposure measures were bariatric surgery before pregnancy and obesity without bariatric surgery, compared with no obesity. The outcome was severe maternal morbidity, a composite of life-threatening pregnancy complications. We estimated risk ratios (RR) and 95% confidence intervals (CI) for the association between bariatric surgery and severe maternal morbidity, adjusted for maternal characteristics. RESULTS: A total of 2654 deliveries (0.1%) were in women who had bariatric surgery, and 70,041 (29.0 per 1000) were in women who had severe maternal morbidity. Risk of severe maternal morbidity was not significantly elevated for bariatric surgery (RR 1.20; 95% CI 0.98-1.46), but was greater for obesity compared with no obesity (RR 1.60; 95% CI 1.55-1.64). Bariatric surgery was not associated with morbidities such as severe preeclampsia, sepsis, and cardiac complications compared with no obesity, but obesity was associated with elevated risks of these and other severe morbidities. Bariatric surgery was associated, however, with intensive care unit admission, compared with no obesity. CONCLUSIONS: Pregnant women with prior bariatric surgery have similar risks as nonobese women for most types of severe maternal morbidity, except for intensive care unit admission.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".