Comparative frequency and prognostic impact of myocardial injury in hospitalized patients with COVID-19 and Influenza
Why this work is in the frame
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Bibliographic record
Abstract
Aims: Myocardial injury (MINJ) in Coronavirus disease 2019 (COVID-19) identifies individuals at high mortality risk but its clinical relevance is less well established for Influenza and no comparative analyses evaluating frequency and clinical implications of MINJ among hospitalized patients with Influenza or COVID-19 are available. Methods and results: Hospitalized adults with laboratory confirmed Influenza A or B or COVID-19 underwent highly sensitive cardiac T Troponin (hs-cTnT) measurement at admission in four regional hospitals in Canton Ticino, Switzerland. MINJ was defined as hs-cTnT >14 ng/L. Clinical, laboratory and outcome data were retrospectively collected. The primary outcome was mortality up to 28 days. Cox regression models were used to assess correlations between admission diagnosis, MINJ, and mortality. Clinical correlates of MINJ in both viral diseases were also identified. MINJ occurred in 94 (65.5%) out of 145 patients hospitalized for Influenza and 216 (47.8%) out of 452 patients hospitalized for COVID-19. Advanced age and renal impairment were factors associated with MINJ in both diseases. At 28 days, 7 (4.8%) deaths occurred among Influenza and 76 deaths (16.8%) among COVID-19 patients with a hazard ratio (HR) of 3.69 [95% confidence interval (CI) 1.70-8.00]. Adjusted Cox regression models showed admission diagnosis of COVID-19 [HR 6.41 (95% CI 4.05-10.14)] and MINJ [HR 8.01 (95% CI 4.64-13.82)] to be associated with mortality. Conclusions: Myocardial injury is frequent among both viral diseases and increases the risk of death in both COVID-19 and Influenza. The absolute risk of death is considerably higher in patients admitted for COVID-19 when compared with Influenza.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it