Effective prognostic and clinical risk stratification in COVID‐19 using multimodality biomarkers
Bibliographic record
Abstract
In acute coronavirus disease 19 (COVID-19) patients, effective clinical risk stratification has important implications on treatment and therapeutic resource distribution. This article reviews the evidence behind a wide range of biomarkers with prognostic value in COVID-19. Patient characteristics and co-morbidities, such as cardiovascular and respiratory diseases, are associated with increased mortality risk. Peripheral oxygen saturation and arterial oxygenation are predictive of severe respiratory compromise, whereas risk scores such as the 4C-score enable multi-factorial prognostic risk estimation. Blood tests such as markers of inflammation, cardiac injury and d-dimer and abnormalities on electrocardiogram are linked to inpatient prognosis. Of the imaging modalities, lung ultrasound and echocardiography enable the bedside assessment of prognostic abnormalities in COVID-19. Chest radiograph (CXR) and computed tomography (CT) can inform about prognostic pulmonary pathologies, whereas cardiovascular CT detects high-risk features such as coronary artery and aortic calcification. Dynamic changes in biomarkers, such as blood tests, CXR, CT and electrocardiogram findings, can further inform about disease severity and prognosis. Despite the vast volumes of existing evidence, several gaps exist in our understanding of COVID-19 biomarkers. First, the pathophysiological basis on which these markers can foretell prognosis in COVID-19 remains poorly understood. Second, certain under-explored tests such as thoracic impedance assessment and cardiovascular magnetic resonance imaging deserve further investigation. Lastly, the prognostic values of most biomarkers in COVID-19 are derived from retrospective analyses. Prospective studies are required to validate these markers for guiding clinical decision-making and to facilitate their translation into clinical management pathways.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".