Does Preoperative MIBI Scanning Lead to Improved Outcomes in Patients Undergoing Abdominal Aortic Aneurysm Surgery?
Bibliographic record
Abstract
Dipyridamole sestamibi nuclear scanning (MIBI) is a commonly used test to screen for cardiac disease in patients undergoing elective abdominal aortic aneurysm (AAA) surgery. However, its routine use for all patients is controversial. The purpose of this study was to determine whether MIBI scanning could identify high-risk patients and lead to decreased myocardial infarction (MI) and cardiac death when compared with patients who did not receive MIBI scanning preoperatively. The authors reviewed 212 consecutive patients undergoing elective AAA repair between January 1990 and December 1993. Data regarding preoperative cardiac status, MIBI scan results, and cardiovascular outcomes were collected. During this period, 92 patients had MIBI scans preoperatively while 120 patients underwent AAA surgery without MIBI scanning. The average ages for these two groups were 70 ±8 and 71 ±9 years, respectively. The frequency of coronary artery disease, angina, and previous MI in the MIBI group was 47%, 26%, and 29%, respectively. In the non-MIBI group, these frequencies were 39%, 23%, and 28%, respectively. Eleven patients were identified in the MIBI group to have moderate or large reversible defects. Of these, five underwent cardiac revascularization with no morbidity. The frequency of postoperative MI and death for the MIBI group was 1.1% (1/92) and 0%, respectively. In the non-MIBI group, it was 3.3% (4/120) and 1.7% (2/120), respectively (p=0.54). Preoperative MIBI scanning identified high-risk patients for AAA surgery. Following coronary revascularization for these high-risk patients, the overall MI and mortality rates were similar to those in patients who did not receive MIBI preoperatively.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".