Bibliographic record
Abstract
Background In up to 2% of all pregnancies, the need for general anesthesia in a nonobstetrical surgery arises. Surgery on a pregnant woman may have significant implications for the fetus, patient, physician and hospital. On review of the plastic surgery literature, the authors were unable to find current guidelines or recommendations for preoperative pregnancy testing in the plastic surgery patient population. Methods Literature regarding maternal and fetal risk during anesthesia and surgery, as well as preoperative pregnancy testing was identified by performing a PubMed, OVID and MEDLINE key word search. The current literature was subsequently reviewed and summarized. Results A report by the American Society of Anesthesiologists Task Force on Preanesthesia Evaluation allows physicians and hospitals to implement their own policies and practices with regard to preoperative pregnancy testing. The overall frequency of an incidentally found positive preoperative pregnancy test ranges from 0.34% to 2.4%. Discussion Various studies have reported increased rates of spontaneous abortions, congenital anomalies, such as neural tube defects, and low and very low birth weight infants born to mothers exposed to anesthesia and surgery during pregnancy. Because the accepted practice is to postpone elective surgery during pregnancy, identifying these patients before surgery is critical. Conclusions Based on the current evidence, the authors' best practice recommendation for preoperative pregnancy testing is provided.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".