Auto-Abs against type I IFNs: Strong, common, and global determinants of severe arboviral diseases
Bibliographic record
Abstract
Human-tropic pathogenic arboviruses are spreading worldwide. There is immense interindividual clinical variability following infection with any arbovirus. Autoantibodies (auto-Abs) neutralizing antiviral type I IFNs (AAN-I-IFN) can underlie a small but growing number of severe arboviral diseases, whether transmitted by ticks (tick-borne encephalitis virus, TBEV; Powassan virus, POWV) or mosquitoes (West Nile virus, WNV; Usutu virus, USUV; Ross River virus, RRV) and whether due to flaviviruses (WNV, TBEV, POWV, and USUV) or alphaviruses (RRV). Evidence is documented in large cohort studies for WNV and TBEV. They can also account severe adverse reactions to the live-attenuated yellow fever virus vaccine. AAN-I-IFN are present before arboviral infection and are the cause of severe disease. Carriers of these auto-Abs are common worldwide (>100 million people), have a very high risk of severe disease (relative risk >100), and account for a sizeable proportion of cases (typically >10%). Other severe diseases due to different arboviruses may also be caused by these auto-Abs.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".