Bibliographic record
Abstract
Abstract The author apply concepts and tools from evolutionary medicine to understanding the SARS-CoV-2 pandemic. The pandemic represents a mismatched conflict, with dynamics and pathology apparently driven by three main factors: (i) bat immune systems that rely on low inflammation but high efficacy of interferon-based defenses; (ii) viral tactics that differentially target the human interferon system, leading to substantial asymptomatic and pre-symptomatic transmission; and (ii) high mortality caused by hyper-inflammatory and hyper-coagulatory phenotypes, that represent dysregulated tradeoffs whereby collateral immune-induced damage becomes systemic and severe. This framework can explain the association of mortality with age (which involves immune life-history shifts towards higher inflammation and coagulation and reduced adaptive immunity), and sex (since males senesce faster than females). Genetic-risk factors for COVID-19 mortality can be shown, from a phenome-wide association analysis of the relevant SNPs, to be associated with inflammation and coagulation; the phenome-wide association study also provides evidence, consistent with several previous studies, that the calcium channel blocking drug amlodipine mediates risk of mortality. Lay Summary: SARS-CoV-2 is a bat virus that jumped into humans. The virus is adapted to bat immune systems, where it evolved to suppress the immune defenses (interferons) that mammals use to tell that they are infected. In humans, the virus can apparently spread effectively in the body with a delay in the production of symptoms and the initiation of immune responses. This delay may then promote overactive immune responses, when the virus is detected, that damage the body as a side effect. Older people are more vulnerable to the virus because they are less adapted to novel infectious agents, and invest less in immune defense, compared to younger people. Genes that increase risk of mortality from SARS-CoV-2 are functionally associated with a drug called amlodipine, which may represent a useful treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".