Troponin T monitoring to detect myocardial injury after noncardiac surgery: a cost–consequence analysis
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Myocardial injury after noncardiac surgery (MINS) is a mostly asymptomatic condition that is strongly associated with 30-day mortality; however, it remains mostly undetected without systematic troponin T monitoring. We evaluated the cost and consequences of postoperative troponin T monitoring to detect MINS. METHODS: We conducted a model-based cost-consequence analysis to compare the impact of routine troponin T monitoring versus standard care (troponin T measurement triggered by ischemic symptoms) on the incidence of MINS detection. Model inputs were based on Canadian patients enrolled in the Vascular Events in Noncardiac Surgery Patients Cohort Evaluation (VISION) study, which enrolled patients aged 45 years or older undergoing inpatient noncardiac surgery. We conducted probability analyses with 10 000 iterations and extensive sensitivity analyses. RESULTS: The data were based on 6021 patients (48% men, mean age 65 [standard deviation 12] yr). The 30-day mortality rate for MINS was 9.6%. We determined the incremental cost to avoid missing a MINS event as $1632 (2015 Canadian dollars). The cost-effectiveness of troponin monitoring was higher in patient subgroups at higher risk for MINS, e.g., those aged 65 years or more, or with a history of atherosclerosis or diabetes ($1309). CONCLUSION: The costs associated with a troponin T monitoring program to detect MINS were moderate. Based on the estimated incremental cost per health gain, implementation of postoperative troponin T monitoring seems appealing, particularly in patients at high risk for MINS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.000 | 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 it