P.144 Awake craniotomy in pregnancy: a systematic review
Bibliographic record
Abstract
Background: Awake Craniotomy during pregnancy is a rare but urgent procedure. Since pregnancy can both accelerate the progression of a tumor and mask other diagnoses, cases may lead to premature termination of pregnancy. From a neurosurgical, anesthetic, and obstetrical perspective, these operations may be challenging. Methods: In accordance with the PRISMA guidelines, MEDLINE, Scopus, and Web of Science databases were searched from inception to January 3rd, 2023. Studies were included if they included pregnant patients who underwent awake craniotomy. Results: Nine papers fit the criteria for the final analysis. All investigations were case studies. A total of nine patients were included. Mean age at surgery was 26.9 years, and mean gestational age at craniotomy was 20.9 weeks. Eight (88.9%) patients underwent craniotomy for tumor resection and the other had a pseudoaneurysm repair. Glioma was the most common tumor pathology (n=5), followed by meningioma (n=1), and glioblastoma (n=1). None of the patients experienced significant intraoperative or immediate postoperative complications. There were no obstetrical complications or significant changes in fetal status during or after surgery, and all reported deliveries were successful with healthy infants. Conclusions: Awake craniotomy during pregnancy can be a safe procedure with appropriate pre-operative patient selection and extensive multidisciplinary planning.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".