Contrast-Enhanced Cardiac Computed Tomography and the Presence of Intravascular Air: A Patient Safety Study
Bibliographic record
Abstract
Background/Objectives: Air embolism on contrast-enhanced computed tomography (CECT) scans may have significant consequences, particularly if a right-to-left shunt is present, as seen in hereditary hemorrhagic telangiectasia. We sought to evaluate the frequency of CECT-associated air emboli in a single tertiary care referral center. Methods: Consecutive non-enhanced and contrast-enhanced cardiac CT studies (NECCT and CECCT, respectively) were evaluated prospectively over a 6-month period. Following the University of Alberta’s Health Research Ethics Board approval (code: Pro00042313; date: 1 May 2014), two experts reviewed all studies independently to assess for the presence and location of air emboli. The control group consisted of only NECCTs. All patients, except for the control group in this study, had an IV cannula placed. When present, the number, volume, and location of air emboli were recorded. Results: In this study, 110 subjects underwent intravenous cannula placement and both NECCT and CECCT. Of these, 27 of the NECCT studies (24.5%) and 36 of the CECCT studies (32.7%) demonstrated intravascular air emboli. Of those with air emboli, the average volume of intravascular gas was 19.22 ± 25.35 µL in the NECCT studies, with most of the intravascular air (70.4%) seen in the right atrial appendage (RAA). The average volume of intravascular air was 14.81 ± 26.54 µL in the CECCT studies, with most of the intravascular air also located within the RAA (72.2%). The incidence of intravascular air was higher in the CECCT group (28.6% increase), with lower volumes of intravascular air. None of the subjects in the control group (n = 28), who underwent NECCT without intravenous cannulation, demonstrated air emboli. Conclusions: Air emboli were present in a significant proportion of subjects undergoing intravenous cannulation and subsequent CECT. The use of CECT should be carefully considered in high-risk populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".