Cardiac Troponin Testing in Patients with COVID-19: A Strategy for Testing and Reporting Results
Bibliographic record
Abstract
BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that emerged late in 2019 causing COVID-19 (coronavirus disease-2019) may adversely affect the cardiovascular system. Publications from Asia, Europe, and North America have identified cardiac troponin as an important prognostic indicator for patients hospitalized with COVID-19. We recognized from publications within the first 6 months of the pandemic that there has been much uncertainty on the reporting, interpretation, and pathophysiology of an increased cardiac troponin concentration in this setting. CONTENT: The purpose of this mini-review is: a) to review the pathophysiology of SARS-CoV-2 and the cardiovascular system, b) to overview the strengths and weaknesses of selected studies evaluating cardiac troponin in patients with COVID-19, and c) to recommend testing strategies in the acute period, in the convalescence period and in long-term care for patients who have become ill with COVID-19. SUMMARY: This review provides important educational information and identifies gaps in understanding the role of cardiac troponin and COVID-19. Future, properly designed studies will hopefully provide the much-needed evidence on the path forward in testing cardiac troponin in patients with COVID-19.
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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 | 0.033 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".