Perioperative cardiac risk reduction in non cardiac surgery
Bibliographic record
Abstract
For patients undergoing nonemergent noncardiac surgery, care must be taken to identify patients at increased risk of major adverse cardiovascular events, as these remain a significant source of perioperative morbidity and mortality. Identification of at-risk patients requires careful attention to risk factors including assessment of functional status, medical comorbidities, and a medication assessment. After identification, to minimize perioperative cardiac risk, care should be taken through a combination of appropriate medication management, close monitoring for cardiovascular ischemic events, and optimization of pre-existing medical conditions. There are multiple society guidelines that aim to mitigate risk of cardiovascular morbidity and mortality in patients undergoing nonemergent noncardiac surgery. However, the rapid evolution of medical literature often creates gaps between the existing evidence and best practice recommendations. In this review, we aim to reconcile the recommendations made in the guidelines from the major cardiovascular and anesthesiology societies from the USA, Canada, and Europe, and to provide updated recommendations based on new evidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".