Simplifying the Noncardiac Surgery Evaluation Pathway
Bibliographic record
Abstract
To the Editor: The revised guidelines for assessment of the cardiac patient for noncardiac surgery, co-published in Anesthesia & Analgesia,1Circulation,2 and the Journal of the American College of Cardiology3 are an area of interest to virtually all anesthesiologists, and many other clinicians involved in preoperative patient assessment. The original flowchart illustrated the recommended management of patients (Fig. 1A). The flowchart can be simplified dramatically, yielding identical outcomes for patients, yet simultaneously being easier to remember and to apply (Fig. 1B). In steps 4 and 5, one decision tree leads to the same outcome for both arms and two outcomes are shared by other pathways as well. Grouping common end-points using binary logic leads to Figure 1B.Figure 1.: Cardiac evaluation and care algorithm for noncardiac surgery based on active clinical conditions, known cardiovascular disease, or cardiac risk factors for patients 50 years of age or greater. A–ORIGINAL FIGURE, B–FIGURE AS REVISED. Developed in Collaboration with the American Society of Echocardiography, American Society of Nuclear Cardiology, Heart Rhythm Society, Society of Cardiovascular Anesthesiologists, Society for Cardiovascular Angiography and Interventions, Society for Vasc, et al. Anesth Analg 2008;106:685–712.We have found this revised figure useful in our institution. We hope this simplified flowchart will increase the utilization of these evidence-based guidelines for the cardiac assessment of the noncardiac surgery patient, and improve patient outcomes. It is worth mentioning that clinicians involved in preoperative patient assessment will also no doubt wish to carefully consider the findings of the recently published POISE trial,4,5 giving further pause to the addition of β-blocker use perioperatively, without a specific indication. Timothy P. Turkstra, MD, M. Eng., FRCPC Philip M. Jones, MD, FRCPC Department of Anesthesia and Perioperative Medicine London Health Sciences Centre–University Hospital London, Ontario, Canada
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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 | 0.011 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".