A quality review of the occurrence of a non-fatal venous air embolism event following CT contrast enhanced administration for the purpose of radiation therapy planning
Bibliographic record
Abstract
Abstract Background The incidence of venous air embolism (VAE) during and following diagnostic and interventional radiographic procedures utilizing contrast media has been well documented in the literature. However to date a case report of a venous air embolism occurring within an outpatient healthcare facility during a contrast enhanced computer tomography radiation therapy planning procedure remains under reported. Purpose Healthcare professionals must remain alerted to the fact that iatrogenic VAE may occur unexpectedly during and following diagnostic and interventional radiographic procedures utilizing the injection of contrast media. The action by all healthcare professionals to implement rapid and clear acute care guidelines will increase the probability of the patient recovering from the event. Materials and methods A review of the aetiology and associated pathophysiology of VAE is provided. This is followed by a detailed case report of the occurrence of a non-fatal VAE event (patient consent was obtained and the consent form template was reviewed by a Research Ethics Board). Conclusion We conclude with a discussion of quality assurance recommendations that should be considered for implementation in an outpatient facility setting that is performing contrast enhanced computer tomography diagnostic, interventional or radiation therapy planning radiographic procedures.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".