Awake craniotomy during pregnancy: A systematic review of the published literature
Why this work is in the frame
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Bibliographic record
Abstract
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. 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. 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 min. The mean gestational age at diagnosis was 13.6 ± 6.5 (2-22) and 19.6 ± 6.9 (9-30) weeks at craniotomy. 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. In experienced hands, AC for resection of cranial lesions of eloquent areas in pregnant patients is safe and feasible and does not alter the pregnancy outcome.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it