Anesthetic Management of Neurosurgical Procedures During Pregnancy
Bibliographic record
Abstract
INTRODUCTION: Neurosurgical disorders are rare during pregnancy and challenge the anesthesiologist with conflicting anesthetic considerations and little evidence to guide decision-making. Our objective was to review the anesthetic management of pregnant patients undergoing intracranial neurosurgery at our institution and to describe the perioperative complications and outcomes. METHODS: We used our institutional Discharge Abstract Database to identify patients assigned both neurological and obstetrical International Classification of Disease 10-A codes between April 1, 2001 and March 1, 2012. Pregnant patients who underwent intracranial neurosurgical procedures underwent a detailed chart review to extract demographic data and details about their anesthetic management and outcome. RESULTS: Nine patients underwent full chart review with a median age of 28 (range, 17 to 35) years and a gestational age of 23 (range, 7 to 30) weeks. Patients underwent a craniotomy for vascular lesions (4), neoplasms (3), and traumatic brain injuries (2). One patient was hyperventilated (PaCO2 28 mmHg), and mannitol and furosemide were used in 6 and 3 patients, respectively, without complication. Maternal neurological outcomes were good in 5 patients (Glasgow Outcome Scale of >3), poor in 3 patients (Glasgow Outcome Scale 3), and 1 patient died. Fetal outcomes were good in 5 patients and poor in 4 patients (1 therapeutic abortion, 3 intrauterine fetal demises). All cases of fetal distress or demise were either remote or occurred before the anesthetic management. CONCLUSIONS: Pregnant patients undergoing neurosurgery experience a high rate of morbidity and mortality. There were no adverse outcomes directly attributed to the use of osmotic diuretics and hyperventilation in our series.
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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 | 0.006 |
| 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 | 0.000 | 0.000 |
| 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".