Avoidable general anesthesia for nonobstetric surgery during pregnancy: a retrospective cohort pilot study (2011–2020)
Bibliographic record
Abstract
BACKGROUND: About 2% of pregnant women are exposed to general anesthesia for nonobstetric surgery. Given the possibility of adverse fetal and maternal effects associated with exposure to general anesthesia, we sought to evaluate the proportion of cases where general anesthesia could have been avoided. METHODS: This single-center pilot retrospective analysis of nonobstetric surgeries performed during pregnancy was conducted at the Caen Normandy University Hospital (2011-2020). An expert panel of seven French anesthesiologists, obstetricians determined whether general anesthesia was avoidable versus required through a majority vote based on an anonymous standardized data collection sheet. General anesthesia was considered avoidable when an alternative such as neuraxial/regional anesthesia or sedation could have been performed. RESULTS: General anesthesia was avoidable in 36/106 (34%) cases of nonobstetric surgery during pregnancy. Endoscopic JJ ureteral stent insertion or removal was the most common procedure where GA was considered avoidable (19/21 cases; 90%). The consensus rates within the expert panel were of 78% for general anesthesia requirement and 71% for general anesthesia avoidability (P=0.7) CONCLUSIONS: A retrospective review of cases by an expert panel identified that general anesthesia for nonobstetric surgery during pregnancy was likely avoidable in one-third of all cases.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".