Bibliographic record
Abstract
Despite an overall decrease in perioperative morbidity and mortality, evidence of some degree of central nervous system dysfunction associated with coronary artery bypass graft (CABG) surgery-with or without cardiopulmonary bypass-has steadily mounted. From preoperative studies of CABG patients, it is apparent that over 50% of patients who present for cardiac surgery have evidence of either extracranial or intracranial atherosclerotic disease. Patient-specific factors thus have a fundamental impact on the risks of a brain injury developing after CABG surgery. Cerebral embolization and/or ischemic hypoperfusion are the most likely etiologic mechanisms for perioperative brain injury associated with cardiac surgery, and these factors are closely interrelated. Various monitoring techniques can decrease risk of intraoperative cerebral embolization and hypoperfusion and are associated with improved outcomes. Ultrasound guided aortic instrumentation (epiaortic scanning) can markedly decrease atheroembolic load and risk of stroke. Unrecognized sources of microgaseous emboli, including air entrainment from surgical purse string sutures and perfusionist interventions, can be identified and reduced by transcranial Doppler monitoring. Cerebral hypoperfusion from unrecognized cerebral venous obstruction, inadequate mean arterial pressure, or from hypocapnic cerebral alkalosis can be identified by multimodality neuromonitoring using regional cerebral oxygen saturation and transcranial Doppler. Overall patient outcomes can be improved, and hospital length of stay shortened, by applied neuromonitoring techniques.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 0.001 |
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".