The Effect of HLA-B27 on Susceptibility and Severity of COVID-19
Bibliographic record
Abstract
To the Editor: Although many genes have polymorphisms, major histocompatibility complex genes are the most polymorphic. Many assume that the diversity of HLA increases the likelihood that a species can survive pandemics. Indeed, evidence suggests that HLA-B27 is protective for HIV1, hepatitis C2, and possibly influenza3. We recently reported results of a Web-based survey involving patients with spondyloarthritis (SpA)4. We now report an additional analysis of these data obtained between April 10, 2020, and May 31, 2020, to determine if the genetic marker HLA-B27 influences the contracting of the coronavirus 2019 (COVID-19) or the severity of COVID-19 infection. Subjects who participated in this research provided electronic consent as a first step in completing the survey. As it was a survey, it was not practical to obtain written, informed consent. Institutional review board approval was received from Oregon Health & Science University (IRB approval number: 00021375). Subjects (n = 3435) from 65 countries diagnosed with SpA completed the survey. Of these, 2836 or 82.6% were aware of their HLA-B27 status, with 76.1% being positive. Of those with known HLA-B27 status, 74.5% were from the United States and 8.0% were from Canada. The median age was 52 years. The group aware of B27 status included 1806 women, … Address correspondence to Dr. J.T. Rosenbaum, Oregon Health & Science University, 3181 SW Sam Jackson Pk Rd, Portland, OR 97239, USA. Email: rosenbaj{at}ohsu.edu.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".