Rationale and design of the long‐Term rIsk, clinical manaGement, and healthcare Resource utilization of stable coronary artery dISease in post–myocardial infarction patients (TIGRIS) study
Bibliographic record
Abstract
The long-term progression of coronary artery disease as defined by the natural disease course years after a myocardial infarction (MI) is an important but poorly studied area of clinical research. The long-Term rIsk, clinical manaGement, and healthcare Resource utilization of stable coronary artery dISease in post-myocardial infarction patients (TIGRIS) study was designed to address this knowledge gap by evaluating patient management and clinical outcomes following MI in different regions worldwide. TIGRIS (ClinicalTrials.gov Identifier: NCT01866904) is a multicenter, observational, prospective, longitudinal study enrolling patients with history of MI 1 to 3 years previously and high risk of developing atherothrombotic events in a general-practice setting. The primary objective of TIGRIS is to evaluate clinical events (time to first occurrence of any event from the composite cardiovascular endpoint of MI, unstable angina with urgent revascularization, stroke, or death from any cause), and healthcare resource utilization associated with hospitalization for these events (hospitalization duration and procedures) during follow-up. Overall, 9225 patients were enrolled between June 2013 and November 2014 and are being followed in 369 different centers worldwide. This will allow for the description of regional differences in patient characteristics, risk profiles, medical treatment patterns, clinical outcomes, and healthcare resource utilization. Patients will be followed for up to 3 years. Here we report the rationale, design, patient distribution, and selected baseline characteristics of the TIGRIS study. TIGRIS will describe real-world management, quality of life (self-reported health), and healthcare resource utilization for patients with stable coronary artery disease ≥1 year post-MI.
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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 | 0.081 | 0.048 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".