Anesthetic considerations for oral maxillofacial surgery in neurologically injured patients: a comprehensive narrative review
Bibliographic record
Abstract
Background and Objective: The co-occurrence of oral-maxillofacial and brain injuries is common and present unique perioperative challenges. This narrative review aims to provide a comprehensive summary of the considerations and best practices related to balancing cerebral oxygen supply with demand, regulating ventilation, upholding hemodynamic stability, sustaining optimal fluid and electrolyte balance, and employing strategies for optimal patient outcomes. Methods: A literature review using the PubMed and Google Scholar databases was employed to gather data from controlled trials, cohort studies, and meta-analyses on neurological injury and oral and maxillofacial surgery (OMFS). Relevant keywords included traumatic brain injury, facial trauma, airway management, pain management, prophylactic anticoagulation, and intracranial pressure. Titles and abstracts were screened for their relevance to the research question and included articles were assessed in full. Articles written in English were prioritized, and the data was categorized into pertinent themes. A narrative synthesis was conducted for each category to ensure a balanced and comprehensive overview of the current literature and guidelines from professional organizations. Key Content and Findings: We identified key elements in secondary brain injury pathophysiology and discussed approaches to minimize delayed neurological injury. Trauma patients at risk for blunt cerebrovascular injuries are discussed and the diagnostic approaches and management options outlined. We have also focused on airway concerns, hemodynamic considerations, and optimization of coagulation and cellular homeostasis in this cohort. Perioperative goals and anesthetic options for neurological injury patients requiring OMFS surgery are highlighted, emphasizing hemodynamic stability and cerebral perfusion. Comprehensive perioperative and postoperative care to improve cerebral perfusion and cellular homeostasis are crucial for optimal outcomes. Conclusions: Patients with neurologic injuries requiring OMFS necessitate specialized perioperative care. Understanding the pathophysiology, potential complications, and best practices for management can optimize patient outcomes. This narrative review serves as a thorough guide for clinicians involved in the care of these patients.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".