Venous Air Embolism During Changes in Patient Position or Ventilation: An Etiology for Postoperative Cardiovascular Collapse?
Bibliographic record
Abstract
In Response: We thank Drs. Seubert and Gravenstein for their appreciation of our findings and their comments. We can only agree with their comment that movement of previously entrained air entering the circulation during other surgery at risk for air embolism (such as hip arthroplasty), at the end of surgery, or after surgery due to changing ventilation patterns (such as positive end-expiratory pressure release), or change of patient positions can severely impair the patient’s hemodynamic stability. Intraoperative transesophageal echocardiography (TEE) is not only the most sensitive monitor of venous air embolism, but also the most useful monitor to visualize directly the cardiac performance. It is this double function of TEE, the recognition of venous air embolism in the heart and the possibility to observe to what degree the cardiac function is affected, which makes TEE so useful in our daily practice. We can only hope that the increasing perception of how frequent venous air embolism really is and that it can occur with so many different types of surgery will lead us to further advance the distribution of TEE devices and to train not only cardiac anesthesiologists in this immensely important technique. The essential message of our investigation is to use TEE in all surgery at risk for venous air embolism and to continue TEE monitoring after surgery until the patient is in the supine position and positive end-expiratory pressure has been released to ensure that sudden hemodynamic problems due to reoccurrence of air in the circulation are not missed. Thomas M. Hemmerling, MD, DEAA Hubert Schmitt, MD
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.020 | 0.024 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".