Prognostic value of severe acute respiratory syndrome coronavirus‐2 viral load and antibodies in patients hospitalized with <scp>COVID</scp>‐19
Bibliographic record
Abstract
Observational studies have identified the potential prognostic value for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) viral load and anti-SARS-CoV-2 antibodies in coronavirus disease 2019 (COVID-19). However, viral load in nasopharyngeal (NP) swabs produced inconsistent results in prognostic analyses, and the prognostic value of viral load or antibodies has not been confirmed in large clinical trials. COVACTA and REMDACTA were double-blind, randomized, controlled trials with a combined enrollment of 1078 patients hospitalized with COVID-19 treated with tocilizumab or placebo in COVACTA or tocilizumab plus remdesivir or placebo plus remdesivir in REMDACTA. We assessed the potential prognostic value of NP and serum SARS-CoV-2 viral load and serum anti-SARS-CoV-2 antibodies at baseline as biomarkers for clinical outcomes in patients enrolled in these trials. In adjusted Cox proportional hazard models, serum viral load was a more reliable predictor of clinical outcomes than NP viral load; high serum viral load was associated with higher risk for death and mechanical ventilation/death and lower likelihood of hospital discharge (high vs. negative viral load hazard ratios [95% confidence interval {CI}] were 2.87 [1.57-5.25], 3.86 [2.23-6.68], and 0.23 [0.14-0.36], respectively, in COVACTA and 8.11 [2.95-22.26], 10.29 [4.5-23.55], and 0.21 [0.15-0.29], respectively, in REMDACTA) and high serum viral load correlated with levels of inflammatory cytokines and lung damage biomarkers. High anti-SARS-CoV-2 spike protein antibody (ACOV2S) levels were associated with higher likelihood of hospital discharge (high vs. below the limit of quantification hazard ratios [95% CI] were 2.55 [1.59-4.08] for COVACTA and 1.54 [1.13-2.09] for REMDACTA). These results support the role of baseline SARS-CoV-2 serum viral load and ACOV2S antibody titers in predicting clinical outcomes for patients hospitalized with COVID-19.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".