The seven enigmas of SARS-CoV-2: From the past to the future
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Five years ago, we launched the COVID Human Genetic Effort. Our goal was to explain the clinical variability among SARS-CoV-2-exposed individuals by searching for monogenic inborn errors of immunity (IEI) and their phenocopies. We deciphered the pathogenesis of critical COVID-19 pneumonia and multisystemic inflammatory syndrome in children (MIS-C) in ~15% and 2% of cases, respectively, thereby revealing general mechanisms of severe disease. We also defined neuro-COVID-19 genetically and immunologically in one child, while we delineated the immunological mechanisms of COVID-toes in healthy children and young adults, paving the way for their genetic study. Understanding the human genetic and immunological basis of resistance to SARS-CoV-2 infection, long COVID, and myocarditis post mRNA vaccination, has been challenging and investigations remain ongoing. This work highlights the power of patient-based basic research and large-scale international collaborative efforts to discover human genetic and immunological drivers of infectious disease phenotypes, with implications for the timely development of new medical strategies before the next pandemic arrives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it