Role of Creatine Kinase in the Troponin Era: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: The diagnosis of non-ST-elevated myocardial infarction (NSTEMI) depends on a combination of history, electrocardiogram, and cardiac biomarkers. The most sensitive and specific biomarkers for cardiac injury are the troponin assays. Many hospitals continue to automatically order less sensitive and less specific biomarkers such as creatine kinase (CK) alongside cardiac troponin (cTn) for workup of patients with chest pain. The objective of this systematic review was to identify whether CK testing is useful in the workup of patients with NSTEMI symptoms. METHODS: We undertook a systematic review to ascertain whether CK ordered as part of the workup for NSTEMI was useful in screening patients with cardiac chest pain. The MEDLINE, Embase, and Cochrane databases were searched from January 1995-September 2020. Additional papers were added after consultation with experts. We screened a total of 2,865 papers, of which eight were included in the final analysis. These papers all compared CK and cTn for NSTEMI diagnosis. RESULTS: In each of the eight papers included in the analysis, cTn showed a greater sensitivity and specificity than CK in the diagnosis of NSTEMI. Furthermore, none of the articles published reliable evidence that CK is useful in NSTEMI diagnosis when troponin was negative. CONCLUSION: There is no evidence to continue to use CK as part of the workup of NSTEMI acute coronary syndrome in undifferentiated chest pain patients. We conclude that CK should not be used to screen patients presenting to the emergency department with chest pain.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".