Perioperative Myocardial Infarction
Bibliographic record
Abstract
M ore than 230 million major surgeries are performed annually worldwide, 1 and this number grows continuously.The 30-day mortality associated with moderate-to high-risk noncardiac surgery in recent large cohorts and population-based studies exceeds 2% 2-4 and surpasses 5% in patients at high cardiac risk.5 Cardiac complications constitute the most common cause of postoperative morbidity and mortality, 4,6 having considerable impact on the length and cost of hospitalization.7 As our population ages, more high-risk cardiac patients will undergo surgery, and perioperative myocardial infarction (PMI) can be an increasing problem. Redefinition of PMITraditionally, MI was defined by the World Health Organization criteria, ECG criteria, and cardiac enzymes.Defining PMI, however, is often difficult because most PMIs occur without symptoms in anesthetized or sedated patients, ECG changes are subtle and/or transient, and the creatine kinase-MB isoenzyme has limited sensitivity and specificity because of coexisting skeletal muscle injury.8 Consequently, PMI was often recognized late (postoperative day 3 to 5), resulting in high (30% to 70% 9 ) mortality.Cardiac troponin assays have changed this definition.10 The recent universal definition of MI 11 is based on a rise and/or fall of cardiac biomarkers (preferably troponin) in the setting of myocardial ischemia: cardiac symptoms, ECG changes, or imaging findings.Studies using serial troponin measurements demonstrate that most PMIs start within 24 to 48 hours of surgery during the greatest postoperative stress.[12][13][14][15] Le Manach et al 15 observed early (Ͻ24 hours) and late (Ͼ24 hours) peaks in troponin in 1136 patients after abdominal aortic aneurysmectomy.Yet, 90% of troponin elevations began within Ͻ24 hours. PathophysiologyTwo distinct mechanisms may lead to PMI: acute coronary syndrome and prolonged myocardial oxygen supply-demand imbalance in the presence of stable coronary artery disease (CAD), designated type 1 and type 2 by the universal definition of MI. 11 This distinction is key to therapeutic considerations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".