Development of a novel risk prediction tool for emergency department patients with symptoms of coronary artery disease: A research study protocol
Bibliographic record
Abstract
Abstract Patients with chest pain and symptoms of acute coronary syndromes (ACS) account for over 600,000 emergency department (ED) visits annually in Canada. Over 80% of these patients do not have ACS, and most are discharged from the ED after a thorough evaluation. However, a large proportion of these patients are referred for outpatient objective cardiac testing after ED discharge, even though their short-term risk for major adverse cardiac events (MACE) such as death, new myocardial infarction or need for revascularization is very small. This contributes to substantial low-value healthcare utilization, and limits access for those patients who are more likely to benefit from objective testing. Existing risk prediction tools were developed prior to the advent of high-sensitivity cardiac troponin assays, were derived in non-representative populations and, when applied to ED patients with low cardiac troponin concentrations, systematically overestimate short-term risk of (MACE). This multicenter prospective cohort study will enrol ED patients with chest pain to derive and validate a novel risk prediction tool to accurately identify patients at low risk of MACE and not requiring additional cardiac testing from patients who are likely to benefit from additional cardiac testing. We will enroll 6500 patients at 13 Canadian EDs nd prospectively follow them for 30 days to ascertain a primary outcome of MACE. The risk prediction tool developed in this project will guide safe, efficient, appropriate referrals of ED patients with chest pain.
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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 | 0.047 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".