Antibody therapy reverses biological signatures of COVID-19 progression
Bibliographic record
Abstract
Abstract Understanding who is at risk of progression to severe COVID-19 is key to effective treatment. We studied correlates of disease severity in the COMET-ICE clinical trial that randomized 1:1 to placebo or to sotrovimab, a monoclonal antibody for the treatment of SARS-CoV-2 infection. Several laboratory parameters identified study participants at greater risk of severe disease, including a high neutrophil-lymphocyte ratio (NLR), a negative SARS-CoV-2 serologic test and whole blood transcriptome profiles. Sotrovimab treatment in these groups was associated with normalization of NLR and the transcriptomic profile, and with a decrease of viral RNA in nasopharyngeal samples. Transcriptomics provided the most sensitive detection of participants who would go on to be hospitalized or die. To facilitate timely measurement, we identified a 10-gene signature with similar predictive accuracy. In summary, we identified markers of risk for disease progression and demonstrated that normalization of these parameters occurs with antibody treatment of established infection.
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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 | 0.001 |
| 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) | 0.003 | 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".