Bibliographic record
Abstract
Delirium is the most common complication in patients over 60 years of age after cardiac surgery (up to 57%). Patients suffering from postoperative delirium will also be at increased risk of traumatic falls, longer intensive care unit and hospital stay, psychiatric crisis, hospital re-admission, degradation toward dementia and death. Postoperative delirium has a high emotional cost on families and on the health care system with an average of $60,000 per capita and per year. Despite the importance of postoperative delirium, no single approach has proven effective to prevent it. Our group found that excessively deep general anesthesia supresses the electrical activity of the brain and is associated with more postoperative delirium and even death. Some evidence suggests, without clearly proving, that purposefully avoiding excessively deep general anesthesia can protect from postoperative delirium. The ENGAGES-CANADA study proposes to address this question by comparing the effect of two ways of managing cardiac anesthesia on the reduction of the occurrence of post-operative delirium. One way will be to give general anesthesia for cardiac surgery according to the actual standards of practice. The other way will be to observe brain electrical activity and avoid excessively deep anesthesia by altering the amount of anesthesia to keep patients asleep without suppressing brain electrical activity. We will also measure psychiatric and cognitive factors for up to a year to understand how they relate to the depth of anesthesia and to delirium. Postoperative delirium after cardiac surgery is so detrimental to patients and costly to the health care system that even a small reduction in it’s occurrence would have a large positive impact. The ENGAGES-CANADA study could make a major contribution to the care of older cardiac surgery patients at risk of delirium and its consequences, reduce caregivers suffering and anxiety, and reduce the costs to the health care system.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.204 | 0.028 |
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".