Screening for Acute Myocardial Injury: Creatine Kinase Is Comparable to Myoglobin
Bibliographic record
Abstract
During the last decade, there have been many studies comparing myoglobin and the troponins to creatine kinase MB. Myoglobin was introduced as an early marker, but most studies have not directly compared it to total creatine kinase in any detail. We retrospectively (9/98-5/99) examined 1772 paired samples from 1572 patients drawn in the emergency department to assess the optimum decision limits, sensitivity, specificity, positive predictive values (PPV), and negative predicitve value (NPV) for creatine kinase and myoglobin in predicting acute myocardial injury. Of the admitted patients, 114 had acute myocardial injury, 166 had angina and 89 had non-cardiac chest pain; 1203 patients were discharged. Initially low creatine kinase (<100 IU/l; minimum 19 IU/l) and myoglobin (<100 microg/l; minimum 9.5 microg/l) results were identified in 63.5% and 88.3% of patients, respectively, emphasizing the importance of serial sampling. Receiver operator characteristic analysis demonstrated optimum decision limits at 100 IU/l and 70 microg/l, respectively. These levels were associated with sensitivity/specificity/PPV/NPV of 66/66/13/96 for creatine kinase and 54/85/22/96 for myoglobin. We conclude that both tests are comparable for initial screening of patients with chest pain in the emergency department. Since creatine kinase is faster, cheaper, and more widely available, it is the test of choice for our institution.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".