Myocardial Injury after Noncardiac Surgery and Perioperative Atrial Fibrillation: From Evidence to Clinical Practice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One in 60 patients who undergo major noncardiac surgery dies within 30 days following surgery. The most common cause is cardiac complications, of which myocardial injury after noncardiac surgery (MINS) and perioperative atrial fibrillation (POAF) are common, affecting about 18 and 11% of adults, respectively, after noncardiac surgery. Patients who suffer MINS are at a higher risk of death compared to patients without MINS. Similarly, patients who develop POAF are at a higher risk of stroke and death compared to patients who do not. Most patients who suffer MINS are asymptomatic, and its diagnosis is not possible without routine troponin monitoring. Observational studies support the use of statins and aspirin in the management of patients with MINS. The only randomized controlled trial to date that has specifically addressed the management of MINS was the MANAGE trial that demonstrated the efficacy and safety of intermediate dose dabigatran in this population. There are no specific prediction models for POAF and no randomised controlled trial evidence to guide the specific management of POAF. Management guidelines in the acute period follow the management of nonoperative atrial fibrillation. The role of long-term anticoagulation in this population is still uncertain and should be guided by a shared care decision model with the patient, and with consideration of the individual risk for stroke balanced against the risk of bleeding. In this review, we present a case-based approach to the detection, prognosis, and management of MINS and POAF based on the existing evidence.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 it