Do Certain Biomarkers Predict Adverse Outcomes in Coronavirus Disease 2019?
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Given the rapid spread of the disease, the World Health Organization (WHO) declared the 2019 - 2020 coronavirus outbreak a Public Health Emergency of International Concern (PHEIC) on January 30, 2020, and a pandemic on March 11, 2020. There have been several reports of the limited resources including the lack of intensive care unit (ICU) beds and mechanical ventilators. Thus, biomarkers that predict ICU stay and mortality will be an important tool to appropriately allocate the limited resources. The aim of this review was to identify laboratory markers that can effectively predict the risk of severe infection and increased mortality in COVID-19 cases. We conducted a systematic review of existing literature in six databases to evaluate the predictive value of various biomarkers. We used the keywords "COVID-19", "SARS-CoV-2", "Novel corona virus pneumonia", "Biomarkers", "Adverse outcomes", "Mortality", etc. among many others to refine our search. Several biomarkers were identified to be associated with adverse outcomes in the above studies. These biomarkers can be used as a tool to identify patients at increased risk for adverse outcomes so that the need for aggressive critical care in such patients is met.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".