Integrated Medical and Nursing Perspectives in the Emergency Management of Venous Gas Embolism: A Multidisciplinary Clinical Approach
Bibliographic record
Abstract
Background: Venous Gas Embolism (VGE) is a critical condition characterized by the entrainment of gas into the venous system, often due to iatrogenic causes like central venous catheterization and neurosurgery. It can lead to right ventricular outflow obstruction, cardiovascular collapse, and, via paradoxical embolism, systemic end-organ ischemia. Aim: This article aims to synthesize an integrated, multidisciplinary approach to the emergency management of VGE, emphasizing the collaborative roles of medicine and nursing in rapid diagnosis, stabilization, and treatment to improve patient outcomes. Methods: The review examines the pathophysiology, etiology, and clinical presentation of VGE. It evaluates diagnostic modalities, including continuous capnography and echocardiography, and details a structured management protocol involving immediate source control, patient repositioning, hemodynamic support, and aspiration of intracardiac air. Results: VGE presents with a spectrum of symptoms, from subtle changes in end-tidal CO₂ to fulminant cardiovascular collapse. Key interventions—administering 100% oxygen, placing the patient in the left lateral decubitus position, and aspirating air via a central venous catheter—are time-critical. Hyperbaric oxygen therapy is a crucial adjunct for severe cases, particularly those with neurological involvement. Conclusion: Successful management of VGE hinges on a high index of suspicion and immediate, coordinated action by a multidisciplinary team. A protocolized response that integrates medical and nursing expertise is essential to mitigate this life-threatening emergency's high morbidity and mortality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".