Increasing Severity of Aortic Atherosclerosis in Coronary Artery Bypass Grafting Patients Evaluated by Transesophageal Echocardiography
Bibliographic record
Abstract
BACKGROUND: Atherosclerotic disease in coronary artery bypass grafting (CABG) patients is a potential contributor to complications in the perioperative periods. This study was undertaken to better define how the frequency of aortic atheromatous disease among patients coming for CABG has evolved over the last decade. METHODS: Data from elective patients coming for CABG who underwent transesophageal echocardiography (TEE) examinations following induction of anesthesia were obtained for the years 2002 and 2009. Aortas were graded according to the method of Kronzon, with the following interpretations: normal = grade I, intimal thickening = 2, atheroma of less than 5 mm = 3, atheroma of > 5 mm = 4, and any mobile atheroma = 5. The data of 124 patients who underwent comprehensive exam of the aorta by one cardiac anesthesiologist were gathered and assigned into two groups based on the year TEE was done. Student's t-test was used for statistical analysis. A P value < 0.05 was considered significant. The data were presented as mean ± SD. RESULTS: There was significant difference between group 2002 (2.05 ± 1.28) and group 2009 (2.59 ± 1.11) in atheroma grade (P = 0.013). CONCLUSIONS: Patients coming for CABG in group 2009 exhibited significantly higher grades of aortic atheroma on TEE, compared to group 2002. Understanding the risk of atheroma in the elderly CABG population may help in altering surgical approaches to lessen the risk of catastrophic stroke. Potential options needing further study include the off-pump approach and modification of cross-clamp site and technique as well as other modalities.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".