Intensive care of the patient following open abdominal aortic surgery
Bibliographic record
Abstract
PURPOSE OF REVIEW: Abdominal aortic aneurysms still require open repair despite the advances that endovascular aneurysm repair has made in treating patients with significant operative risk. Older patients with significant comorbidities require open repair of their complex aneurysms when they fail to meet anatomic criteria for endovascular aneurysm repair. This review discusses the physiologic insult of abdominal aortic surgery. It aims to address which patients are the highest risk of postoperative morbidity, and advances in their intensive care unit management to reduce such morbidity. RECENT FINDINGS: Advanced age, chronic health dysfunction, emergency surgery, and multiple organ failure are independent predictors of postoperative mortality. Myocardial ischemia is the largest contributor to patient morbidity, with any rise in postoperative cardiac troponin I predicting increased in-hospital myocardial infarction and mortality. Highest-risk patients benefit most from optimizing perioperative cardiac status with beta-blockade. Perioperative treatment with fenoldopam may improve renal outcome. Tracheostomy to aid in weaning is associated with increased mortality but may improve outcome in patients with preoperative chronic obstructive pulmonary disease. SUMMARY: Demographic trends indicate that open aortic surgery will continue to be performed on older patients with complex aneurysms. Identifying patients at risk and optimizing their postoperative risk factors will improve outcomes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".