Comparison of two biomarker only algorithms for early risk stratification in patients with suspected acute coronary syndrome
Bibliographic record
Abstract
BACKGROUND: We developed a biomarker algorithm encompassing the clinical chemistry score (CCS; which includes the combination of a random glucose concentration, an estimated glomerular filtration rate and high-sensitivity cardiac troponin; hs-cTn) with the Ortho Clinical Diagnostics hs-cTnI assay (CCS-serial) and compared it to the cutoffs derived from Ortho Clinical Diagnostics 0/1 h (h) algorithm for 7-day myocardial infarction (MI) or cardiovascular (CV)-death. METHODS: The study cohort was an emergency department (ED) population (n = 906) with symptoms suggestive of acute coronary syndrome (ACS) who had two Ortho hs-cTnI results approximately 3 h apart. Diagnostic parameters (sensitivity/specificity/negative predictive value; NPV/positive predictive value; PPV) were derived for the CCS-serial and the 0/1 h algorithm for 7-day MI/CV-death. A safety analysis was performed for patients in the rule-out arms of the algorithms for 30-day MI/death. RESULTS: The CCS-serial algorithm yielded 100% sensitivity/NPV (32% low-risk) and 95.7% specificity/65% PPV (11% high-risk). The 0/1 h algorithm-cutoffs yielded sensitivity/NPV/specificity/PPV of 97.8%/99.4%/91.3%/50%, which classified 38% of patients as low-risk and 16% of patients as high-risk. Four patients (1.2%) in the 0/1 h algorithm-cutoff rule-out arm had a 30-day MI/death outcome as compared to zero patients in the CCS-serial rule-out arm (p = 0.06). CONCLUSION: Both the CCS-serial and 0/1 h algorithm cutoffs yield high NPVs with a similar proportion of patients identified as low-risk. These data may be useful for sites who are unable to collect samples at 0/1 h in the emergency department.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".