Cerebral Oximetry Monitoring to Maintain Normal Cerebral Oxygen Saturation during High-risk Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Cerebral oxygen desaturation during cardiac surgery has been associated with adverse perioperative outcomes. Before a large multicenter randomized controlled trial (RCT) on the impact of preventing desaturations on perioperative outcomes, the authors undertook a randomized prospective, parallel-arm, multicenter feasibility RCT to determine whether an intervention algorithm could prevent desaturations. METHODS: Eight Canadian sites randomized 201 patients between April 2012 and October 2013. The primary outcome was the success rate of reversing cerebral desaturations below 10% relative to baseline in the intervention group. Anesthesiologists were blinded to the cerebral saturation values in the control group. Intensive care unit personnel were blinded to cerebral saturation values for both groups. Secondary outcomes included the area under the curve of cerebral desaturation load, enrolment rates, and a 30-day follow-up for adverse events. RESULTS: Cerebral desaturations occurred in 71 (70%) of the 102 intervention group patients and 56 (57%) of the 99 control group patients (P = 0.04). Reversal was successful in 69 (97%) of the intervention group patients. The mean cerebral desaturation load (SD) in the operating room was smaller for intervention group patients compared with control group patients (104 [217] %.min vs. 398 [869] %.min, mean difference, -294; 95% CI, -562 to -26; P = 0.03). This was also true in the intensive care unit (P = 0.02). There were no differences in adverse events between the groups. CONCLUSIONS: Study sites were successful in reversal of desaturation, patient recruitment, randomization, and follow-up in cardiac surgery, supporting the feasibility of conducting a large multicenter RCT.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".