The Interplay Between COVID-19 and Spondyloarthritis or Its Treatment
Bibliographic record
Abstract
OBJECTIVE: The coronavirus disease 2019 (COVID-19) pandemic has created multiple uncertainties regarding rheumatic diseases or their treatment, with regard to the susceptibility to or severity of the viral disease. We aimed to address these questions as they relate to spondyloarthritis (SpA). METHODS: We created a longitudinal survey from April 10, 2020, to April 26, 2021. There were 4723 subjects with SpA and 450 household contacts who participated worldwide. Of these, 3064 respondents were from the US and 70.4% of them provided longitudinal data. To control for the duration of potential risk of COVID-19, the rate of contracting the disease was normalized for person-months of exposure. RESULTS: = 0.06). A paired evaluation using patients and household members did not show a statistically significant effect to indicate a predisposition for developing COVID-19 as a result of SpA or its treatment. Our data failed to show that any class of medication commonly used to treat SpA significantly affected the risk of developing COVID-19 or increasing the severity of COVID-19. CONCLUSION: These data do not exclude a small increased risk of developing COVID-19 as a result of SpA, but the risk, if it exists, is low and not consistently demonstrated. The data should provide reassurance to patients and to rheumatologists about the risk that COVID-19 poses to patients with SpA.
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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 | 0.006 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".