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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.004 | 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".