Neurosurgical Interventions for Neurotrauma in the Obstetric Population: A Systematic Review
Bibliographic record
Abstract
Trauma requiring neurosurgical intervention in the obstetric population is rare. Provision of care must include consideration for both maternal and fetal well-being, and conflicts may arise. Management strategies to reduce elevated maternal intracranial pressure (ICP) and provide adequate surgical exposure, for example, may compromise uteroplacental perfusion. There is scarce literature to guide anesthetic care and few resources summarizing management of these uncommon cases. We conducted a systematic literature search for English publications of neurosurgical interventions on obstetric patients following trauma. We searched MEDLINE, EMBASE, and Google Scholar from inception to May 1, 2019. We identified 18 cases from 13 publications including 9 case reports and 4 case series. Median Glasgow coma scale on presentation was 6, good maternal outcome occurred in 39% of cases, and good fetal outcome occurred in 67% of cases. Qualitative review of the articles suggests an initially low Glasgow coma scale on admission commonly resulted in worse maternal and fetal outcomes. Delivery occurred postneurosurgical intervention in the majority of viable fetuses. Few details were available regarding anesthetic management, and ICP management strategies varied widely. Our review identified only a small number of case reports and case series. Maternal outcomes were generally poor, although the majority of fetal outcomes were good. Although there seems to be a relationship between outcomes and severity of maternal injury on presentation, it is difficult to draw conclusions or make recommendations because of limited data on perioperative anesthetic and ICP management strategies. Regardless of gestational age, maternal supremacy must be upheld. Our results are limited by the quality of the available research and potential selection bias.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".