Efficacy and safety of a 0/1-h high-sensitivity cardiac troponin I (hs-cTnI) protocol to rule out acute coronary syndrome over a one-year period
Bibliographic record
Abstract
Aim: Evaluate the diagnostic and prognostic performance of the ESC 0/1H Algorithm and its utility in safely discharging patients with suspected Acute Coronary Syndrome (ACS) in the Emergency Department. Methods: This analysis is a retrospective cohort study of 3,156 patients presenting to an academic medical center emergency department (ED) between May 20, 2019 and May 31, 2020. After completing the Beckman Coulter Access High Sensitivity Troponin I (hs-TnI) assay per current institution protocol (T0, T1H, T3H), patients identified to have symptoms concerning ACS were retrospectively analyzed using the ESC 0/1H Algorithm to assess the safety and efficacy of a rule-out algorithm for early discharge. Results: The negative predictive value (NPV) of the protocol (T0 < 6 pg/mL; or females with T0 ≤ 15 pg/mL and T1 ≤ 15 pg/mL; or males with T0 ≤ 20 pg/mL and T1 ≤ 20 pg/mL) was 99.4% (95%CI: 99.0-99.7%) for acute myocardial injury at the initial ED visit (index ED visit) and/or 30-day cardiac-related death. The negative likelihood ratio was 0.08 (95%CI: 0.05%-0.13%), and the specificity was 86.3% (95%CI: 85.0-87.5%).Conclusion: The protocol was found to have a NPV greater than 99% and a negative likelihood ratio of 0.08, suggesting it is safe to use for patients presenting to the ED with ACS symptoms no matter the time of symptom onset.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".