Assessing the Requirement for the 6-Hour Interval between Specimens in the American Heart Association Classification of Myocardial Infarction in Epidemiology and Clinical Research Studies
Bibliographic record
Abstract
BACKGROUND: The American Heart Association (AHA) case definition for acute myocardial infarction (AMI) requires an "adequate set" of biomarkers: 2 measurements of the same marker at least 6 h apart. A sensitive troponin assay might detect significant changes in concentration earlier. We determined AMI prevalence, using protocols with shorter intervals between measurements, with and without incorporating the time from onset of symptoms. METHODS: The AHA case definition was used to retrospectively assign a diagnosis in 258 patients presenting to the emergency department with symptoms of cardiac ischemia. AMI was diagnosed if either specimen in an adequate set had a cardiac troponin I (cTnI) above the 99th percentile (AccuTnI >0.04 microg/L; Beckman Coulter) with a > or =20% change in concentration between specimens. We assessed positivity for AMI after progressively decreasing the time interval between specimens in specimen sets. In addition, for each patient, 2 additional specimen pairs were selected: pairs collected at least 1 h apart with 1 specimen being either > or =3 h after onset or > or =6 h after onset. RESULTS: When we used the AHA definition, the AMI prevalence was 35.7%. Prevalence was not significantly diminished when the interval between specimens was > or =5, > or =4, or > or= 3 h (36.4%, 34.5%, and 33.7%, respectively) compared with the AHA > or =6 h interval. When the time from onset of symptoms was included in the specimen selection algorithm, a 1-h interval was sufficient provided that at least one specimen was collected > or =6 h after onset (prevalence, 34.1%; P = 0.48 vs AHA definition). CONCLUSION: A sensitive cTnI assay in specimen sets with time intervals > or =3 h, or having one specimen > or =6 h after onset, gave an AMI prevalence equivalent to the AHA definition.
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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 | 0.066 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".