Anesthesiology: Resetting Our Sights on Long-term Outcomes: The 2020 John W. Severinghaus Lecture on Translational Science
Bibliographic record
Abstract
Anesthesiologists have worked relentlessly to improve intraoperative anesthesia care. They are now well positioned to expand their horizons and address many of the longer-term adverse consequences of anesthesia and surgery. Perioperative neurocognitive disorders, chronic postoperative pain, and opioid misuse are not inevitable adverse outcomes; rather, they are preventable and treatable conditions that deserve attention. The author's research team has investigated why patients experience new cognitive deficits after anesthesia and surgery. Their animal studies have shown that anesthetic drugs trigger overactivity of "memory-blocking receptors" that persists after the drugs are eliminated, and they have discovered new strategies to preserve brain function by repurposing available drugs and developing novel therapeutics that inhibit these receptors. Clinical trials are in progress to examine the cognitive outcomes of such strategies. This work is just one example of how anesthesiologists are advancing science with the goal of improving the lives of patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".