Outcomes associated with antiphospholipid antibodies in COVID-19: A prospective cohort study
Bibliographic record
Abstract
Background: The significance of antiphospholipid antibodies (aPL) in COVID-19 remains uncertain. Objectives: We determined whether aPL are associated with COVID-19 and/or thrombosis or adverse outcomes during hospitalization for COVID-19. Methods: Symptomatic adults tested for SARS-CoV-2 for clinical reasons (March-July 2020) with either ≥1 positive polymerase chain reaction (COVID-19+) or all negative (non-COVID-19) results were recruited to a biobank collecting plasma, clinical data, and outcomes. We tested baseline plasma samples (days 0-7) of all subjects (and day-30 samples in the COVID-19+ subjects, when available) for aPL (anticardiolipin immunoglobulin [Ig]M/IgG, anti-β2-glycoprotein I IgM/IgG, antiphosphatidylserine/prothrombin IgM/IgG, and lupus anticoagulant). We compared the baseline prevalence of aPL between the COVID-19+ and non-COVID-19 subjects. Among hospitalized COVID-19+ subjects, multivariable logistic regression was used to evaluate the association of aPL (and their subtypes) with arterial or venous thromboembolic events, acute kidney injury, intensive care unit admission, mechanical ventilation, and death after adjusting for potential confounders. Results: = 241), baseline aPL positivity was associated with acute kidney injury (odds ratio [OR], 1.8; 95% CI, 1.1-3.2) and mechanical ventilation (OR, 3.2; 95% CI, 1.5-6.8) but not death (OR, 1.2; 95% CI, 0.6-2.5). In secondary analyses, medium-to-high titers of anticardiolipin IgG (>40) were associated with thromboembolic events (OR, 7.3; 95% CI, 1.8-30.1). Conclusion: In patients with COVID-19, aPL may help identify an increased risk of thrombosis and other adverse outcomes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".