Bibliographic record
Abstract
Neurologic damage after cardiac surgery remains an important cause of postoperative morbidity. In addition to a wide variety of procedural risks, patient-specific factors such as the presence of extracranial or intracranial atherosclerotic disease, either alone or together, have a fundamental impact on the risk of brain injury developing after cardiovascular surgery. A variety of neurophysiologic monitoring techniques have been used during cardiovascular surgery in hopes of averting neurologic injury. In this issue of Seminars, the strengths and weaknesses of each are discussed by a group of highly experienced clinical investigators. The ultrasound techniques of epiaortic scanning and continuous transcranial Doppler insonation of large intracranial arteries can alter perfusion management and surgical habits to markedly decrease the delivery of atherosclerotic, lipoidal, and gaseous microemboli to the brain and other vital organs. Cerebral hypoperfusion from unrecognized cerebral venous obstruction, inadequate mean arterial pressure, or hypocapnic cerebral alkalosis can be identified by transcranial near-infrared spectroscopy, electroencephalogram, and sensory evoked potentials. Compromise of spinal cord perfusion during the repair of thoracoabdominal aneurysms may be identified and corrected with the guidance provided by transcranial electric motor-evoked potentials. Quantitative electroencephalogram and auditory evoked potential indices also appear beneficial in producing objective measures of the hypnotic component of anesthesia. These neuromonitoring methods, particularly when used in concert, can improve overall patient outcome and reduce hospital length of stay.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".