Awake Craniotomy During Pregnancy: A Systematic Review of The Published Literature
Bibliographic record
Abstract
Abstract Purpose Neurosurgical pathologies in pregnancy pose significant complications for the patient and fetus, and physiological stressors during anesthesia and surgery may lead to maternal and fetal complications. Awake craniotomy (AC) can preserve neurological functions while reducing exposure to anesthetic medications. We reviewed the literature investigating AC during pregnancy.Methods PubMed, Scopus, and Web of Science databases were searched from the inception to February 7th, 2023, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Studies in English investigating AC in pregnant patients were included in the final analysis.Results Nine studies composed of nine pregnant patients and ten fetuses (one twin-gestating patient) were included. Glioma was the most common pathology reported in six (66.7%) patients. The frontal lobe was the most involved region (4 cases, 44.4%), followed by the frontoparietal region (2 cases, 22.2%). The awake-awake-awake approach was the most common protocol in seven (77.8%) studies. The shortest operation time was two hours, whereas the longest one was eight hours and 29 minutes. The mean gestational age at diagnosis was 13.6 ± 6.5 (2–22) and 19.6 ± 6.9 (9–30) weeks at craniatomy. Seven (77.8%) studies employed intraoperative fetal heart rate monitoring. None of the AC procedures was converted to general anesthesia. Ten healthy babies were delivered from patients who underwent AC.Conclusions In experienced hands, AC for resection of cranial lesions in pregnant patients is safe and feasible and does not alter the pregnancy outcome.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".