Human Auditory Evoked Potentials in the Assessment of Brain Function During Major Cardiovascular Surgery
Bibliographic record
Abstract
Focal neurologic and intellectual deficits or memory problems are relatively frequent after cardiac surgery. These complications have been associated with cerebral hypoperfusion, embolization, and inflammation that occur during or after surgery. Auditory evoked potentials, a neurophysiologic technique that evaluates the function of neural structures from the auditory nerve to the cortex, provide useful information about the functional status of the brain during major cardiovascular procedures. Skepticism regarding the presence of artifacts or difficulty in their interpretation has outweighed considerations of its potential utility and noninvasiveness. This paper reviews the evidence of their potential applications in several aspects of the management of cardiac surgery patients. The sensitivity of auditory evoked potentials to the effects of changes in brain temperature makes them useful for monitoring cerebral hypothermia and rewarming during cardiopulmonary bypass. The close relationship between evoked potential waveforms and specific anatomic structures facilitates the assessment of the functional integrity of the central nervous system in cardiac surgery patients. This feature may also be relevant in the management of critical patients under sedation and coma or in the evaluation of their prognosis during critical care. Their objectivity, reproducibility, and relative insensitivity to learning effects make auditory evoked potentials attractive for the cognitive assessment of cardiac surgery patients. From a clinical perspective, auditory evoked potentials represent an additional window for the study of underlying cerebral processes in healthy and diseased patients. From a research standpoint, this technology offers opportunities for a better understanding of the particular cerebral deficits associated with patients who are undergoing major cardiovascular procedures.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".