Cardiac Troponin T Elevation Predicts Mortality in Hospitalized COVID-19 Patients
Bibliographic record
Abstract
Abstract Objective To evaluate if cardiac troponin values predict poor outcomes in COVID-19 patients across the range of patients of different sex and age. Methods We examined high-sensitivity cardiac troponin T (hs-cTnT) levels in 1,050 severely ill hospitalized COVID-19 patients who had hs-cTnT data available and participated in the Expanded Access Program for convalescent plasma study during the first wave (April-August 2020) of the COVID-19 pandemic. Results We observed a continuous relationship between hs-cTnT levels and mortality in hospitalized males and females with COVID-19. This finding was present regardless of sex or age. Conclusion These data indicate the prognostic ability of hs-cTnT to predict mortality in hospitalized COVID-19 patients across all relevant patient groups. Clinical Trials registration number NCT04338360 Clinical Perspective This study evaluated the prognostic value of elevated hs-cTnT in patients hospitalized with COVID-19 who received convalescent plasma. There was a continuous relationship between elevated hs-cTnT values and mortality in both males and females hospitalized for COVID-19. The magnitude of the prognostic value of elevated hs-cTnT varied by sex and age, suggesting that these covariates are important to consider in this population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".