Classification and risk stratification of patients with acute chest pain using a low discriminatory level of cardiac troponin T
Bibliographic record
Abstract
BACKGROUND: Cardiac troponins are the biochemical markers of choice for the evaluation of acute coronary syndromes (ACS). Using the first-generation test, most studies related adverse outcome to > 0.20 or 0.10 microg/l cardiac troponin T (cTnT) levels. With the highly sensitive and specific second- and third-generation assays, cTnT is undetectable in most healthy individuals. HYPOTHESIS: We evaluated whether a lower cTnT level, within 24 h of admission, could indicate an increased risk of future complications. METHODS: During 1998-1999, clinical data were collected in 260 patients with ACS. Cardiac troponin T was measured at arrival, and 4, 8, and 12-24 h thereafter. The maximum cTnT value was then used to assess, over a 15-month follow-up period, the cumulative risk of death or myocardial infarction (MI), as well as rates of events according to quartiles of cTnT values. RESULTS: Patients with < or = 0.03 microg/l cTnT levels had the lowest rate of adverse events and the best Kaplan-Meier event-free survival curve. Increasing cTnT levels were associated with stepwise increases in mortality rates and with a constant 10-fold increase in MI rates during follow-up. CONCLUSIONS: A low threshold cTnT elevation is recommended to assess the risk of ACS. All cTnT elevations > 0.03 microg/l predict a higher risk of MI during follow-up, whereas increasing values predict mortality in relation to the amount of elevation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".