Bibliographic record
Abstract
Cardiac surgery is increasingly performed on elderly patients with extensive coronary artery abnormalities who have impaired left ventricular function, decreased physiologic reserve, and multiple comorbid conditions. Considerable numbers of these patients develop perioperative neurologic complications ranging from subtle cognitive dysfunction to more evident postoperative confusion, delirium, and, less commonly, clinically apparent stroke. Magnetic resonance imaging studies have elucidated that a considerable number of patients have new ischemic brain infarcts, particularly after conventional coronary artery bypass graft surgery. Mechanisms of cerebral injury during and after cardiac surgery are discussed. Intraoperative transesophageal echocardiography and epiaortic scanning for detection of atheromatous disease of the proximal thoracic aorta is paramount in identifying patients at high risk from neurologic injury. It is important to recognize that our efforts to minimize neurologic injury should not be limited to the intraoperative period. Particular efforts should be directed to temperature management, glycemia control, and pharmacologic neuroprotection extending into the postoperative period. Preoperative magnetic resonance angiography may be of value for screening patients with significant atheroma of the proximal thoracic aorta. It is likely that for patients with no significant atheromatous disease, conventional coronary artery revascularization is the most effective long-term strategy, whereas patients with atheromatous thoracic aorta may be better managed with beating heart surgery, hybrid techniques, or medical therapy alone. Patient stratification based on the aortic atheromatic burden should be addressed in future trials designed to tailor treatment strategies to improve long-term outcomes of coronary heart disease and reduce the risks of perioperative neurologic injury.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".