Is it better to shine a light, or rather to curse the darkness? Cerebral near-infrared spectroscopy and cardiac surgery
Bibliographic record
Abstract
Cerebral perfusion and good neurological outcome appear most obviously at risk during operations on the aortic arch. However, it is also evident that from a numerical standpoint, otherwise uncomplicated coronary artery bypass grafting (CABG) accounts for the greatest number of perioperative strokes, as shown in a review of 7839 patients in which it was determined that the overall incidence of clinical stroke immediately apparent at extubation was 1.4% and that in addition to severe aortic calcification, cardiopulmonary bypass (CPB) time was also an independent risk factor [1]. Similarly, in a previous study of 13 897 CABG patients, it was also noted that the duration of CPB increased the risk of hypoperfusion strokes and that ‘nearly 75% of all strokes occurred among the 90% of patients at low or medium preoperative risk’ [2]. These facts indicate that the duration of CPB and inadvertent cerebral hypoperfusion can directly influence the risk of perioperative stroke and adverse neurological outcomes. Clearly, for most patients undergoing cardiac surgery, survivorship is of first importance, but it is also readily apparent that for many patients, the risk of severe neurological injury, stroke and significant cognitive impairment are ever-present and particularly feared concerns [3].
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".