Physicians Should Obtain Perioperative Cardiac Troponin Measurements in At-Risk Patients Undergoing Noncardiac Surgery
Bibliographic record
Abstract
Surgery is common; the average American will undergo 7 operations during their lifetime (1). Although surgery has the potential to improve and prolong the quality and duration of life, postoperative death is the third leading cause of death worldwide (2). Approximately half of perioperative deaths are vascular, and myocardial ischemic injuries are the most common cause of perioperative vascular deaths (3). Most perioperative myocardial ischemic injuries are clinically asymptomatic, and without troponin monitoring after noncardiac surgery, these prognostically important complications will go unrecognized (4). Here, we review and comment on the evidence for monitoring perioperative troponin measurements in at-risk patients undergoing noncardiac surgery. In a 1938 publication, Master and colleagues reported a case series of patients who had perioperative myocardial infarctions (MI), and 60% of the patients did not experience ischemic pain (5). The authors stated the lack of ischemic symptoms, “may be accounted for, in part, by the liberal use of narcotics and sedatives after operation” (5). This study, from over 80 years ago, identified the challenge in diagnosing perioperative MI (i.e., most events are asymptomatic).
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".