Adverse Fetal Outcomes and Maternal Mortality Following Nonobstetric Abdominopelvic Surgery in Pregnancy
Bibliographic record
Abstract
OBJECTIVE: To quantify the absolute risks of adverse fetal outcomes and maternal mortality following nonobstetric abdominopelvic surgery in pregnancy. SUMMARY BACKGROUND DATA: Surgery is often necessary in pregnancy, but absolute measures of risk required to guide perioperative management are lacking. METHODS: We systematically searched MEDLINE, EMBASE, and EvidenceBased Medicine Reviews from January 1, 2000, to December 9, 2020, for observational studies and randomized trials of pregnant patients undergoing nonobstetric abdominopelvic surgery. We determined the pooled proportions of fetal loss, preterm birth, and maternal mortality using a generalized linear random/mixed effects model with a logit link. RESULTS: We identified 114 observational studies (52 [46%] appendectomy, 34 [30%] adnexal, 8 [7%] cholecystectomy, 20 [17%] mixed types) reporting on 67,111 pregnant patients. Overall pooled proportions of fetal loss, preterm birth, and maternal mortality were 2.8% (95% CI 2.2-3.6), 9.7% (95% CI 8.3-11.4), and 0.04% (95% CI 0.02-0.09; 4/10,000), respectively. Rates of fetal loss and preterm birth were higher for pelvic inflammatory conditions (eg, appendectomy, adnexal torsion) than for abdominal or nonurgent conditions (eg, cholecystectomy, adnexal mass). Surgery in the second and third trimesters was associated with lower rates of fetal loss (0.1%) and higher rates of preterm birth (13.5%) than surgery in the first and second trimesters (fetal loss 2.9%, preterm birth 5.6%). CONCLUSIONS: Absolute risks of adverse fetal outcomes after nonobstetric abdom- inopelvic surgery vary with gestational age, indication, and acuity. Pooled estimates derived here identify high-risk clinical scenarios, and can inform implementation of mitigation strategies and improve preoperative counselling.
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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 | 0.020 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".