Preventing, identifying and managing myocardial injury after non cardiac surgery – a narrative review
Bibliographic record
Abstract
PURPOSE OF REVIEW: There is mounting and convincing evidence that patients with postoperative troponin elevation, with or without any clinical symptoms, are at higher risk for both, short- and long-term morbidity and mortality. Myocardial injury after noncardiac surgery (MINS) is a relatively newly described syndrome, and the pathogenesis is not fully understood yet. MINS is now an established syndrome and multiple guidelines address potential etiologies, triggers, as well as preventive and management strategies. RECENT FINDINGS: Surveillance in high-risk patients is required, as most MINS would otherwise be missed. There is no reliable and established preventive strategy, but several potentially avoidable triggers like hypotension, pain and anemia have been identified. Managing patients with MINS postoperatively includes minimizing triggers (such as hemodynamic abnormalities and anemia) that can continue the damage. Long-term pharmacologic strategies include beta-blockers, statins, antiplatelet agents, and anticoagulation. SUMMARY: MINS affects up to 20% of surgical patients, remains clinically mostly silent, but is associated with elevated morbidity and mortality. A multidisciplinary approach, that includes involvement of anesthesiologists, for the prevention, diagnosis, and treatment of MINS is recommended.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".