Bibliographic record
Abstract
Troponin elevations are frequent during critical illness and associated with higher short-term mortality. Whether troponin elevations in that population independently confer a worse prognosis remains a matter of debate and how to manage patients with troponin elevations in the intensive care unit is unknown. Myocardial injury after noncardiac surgery is a well-defined entity, but can the same criteria be applied in patients who transition in the intensive care unit? Most patients present a troponin elevation early after coronary artery bypass surgery. How should a myocardial infarction be defined in these patients? This thesis comprises 7 chapters that inform these knowledge gaps. Chapter 1 is an introduction providing the rationale for conducting each of the included studies. Chapter 2 reports on the PROTROPIC pilot study evaluating the feasibility of a larger study to assess whether troponin elevations in critical illness independently predict mortality. Chapter 3 presents the use of secondary cardiovascular prevention medications and cardiac risk stratification in the PROTROPIC pilot study participants. Chapter 4 is a systematic review and meta-analysis of randomized controlled trials evaluating the efficacy and safety of statins in critically ill patients. Chapter 5 describes patients admitted to the intensive care unit after noncardiac surgery in the VISION cohort. This substudy also evaluates whether admission to the intensive care unit modifies the prognosis associated with myocardial injury after noncardiac surgery. Chapter 6 evaluates the prevalence and prognosis associated with different definitions of myocardial infarction after coronary artery bypass grafting using data from the CORONARY trial. Finally, Chapter 7 discusses the conclusion, limitation, and implications of the research presented in this PhD thesis.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".