Prof. Davy Cheng: Evidenced-based perioperative management in cardiac surgery
Bibliographic record
Abstract
Prof. Davy Cheng (Figure 1) is recognized as a world expert in perioperative outcomes and resource utilization in cardiovascular anesthesia and surgery, blood management, minimally invasive and robotic cardiac surgery, and perioperative evidence-based medicine. His pioneer work in fast track cardiac anesthesia and recovery has become the standard of cardiac anesthesia and recovery practice around the world. Prof. Cheng established the Evidence-Based Perioperative Clinical Outcomes Research Group (EPiCOR) and the MEDICI Centre (Medical Evidence, Decision Integrity, Clinical Impact) at Western and London Teaching Hospitals, Canada and continues to release a number of pivotal publications to direct evidence-based medical and surgical practice. He is a recognized healthcare leader in the forefront of research, practice and healthcare policy. In 2014, he has received the honor of Elected Honorary Member, The German Society of Anaesthesiology and Intensive Care Medicine (DGAI) and Canadian Society of Physician Executives Excellence in Medical Leadership Award for his great contribution to the field of anesthesiology. Annals of Translational Medicine (ATM) editor was much honored to invite him to share some of his insights in the 2014 Chinese Heart Congress (CHC).
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".