Meta analysis on the prevalence of autoantibody responses in COVID-19 patients
Bibliographic record
Abstract
Objectives: An infection with severe acute respiratory syndrome coronavirus (SARS-CoV-2) may play a significant role in the pathogenesis of autoimmune rheumatic diseases, interactions between the virus and defence mechanisms may promote the development of autoimmune processes. Studies have reported elevated levels of autoimmune antibodies in patients with Coronavirus-Induced Disease-19 (COVID-19) infection, however the prevalence is not well documented. We aimed to assess the prevalence of autoantibodies in COVID-19 patients compared with unaffected subjects. Methods: Electronic searches were performed using the Cochrane Library, PubMed, Embase, Web of Science, Chinese Biological Medicine Database (CBM), China National Knowledge Infrastructure (CNKI), WANFANG and Chinese Weipu (VIP) databases. The case-control studies which examined the autoantibodies in the serum of COVID-19 patients and control subjects, published before September, 2024, were included in this meta-analysis. The literatures were strictly screened according to the inclusion and exclusion criteria. Quality assessment was performed using a modified version of the Newcastle-Ottawa scale (NOS). The odds ratios (OR) of seropositivity to autoantibodies were calculated using Rev Man 5.3. The stability was evaluated by sensitivity analysis. Egger test was used to evaluate the publication bias. Results: A total of 12 articles involving 1176 COVID-19 patients and 909 control subjects met eligibility criteria for inclusion in our meta-analysis. An overall OR for antinuclear antibodies (ANAs) was 2.53 [95% confidence interval (CI) 1.06-6.03], that for anti-cardiolipin antibodies (ACAs) was 3.05 (95% CI 1.48-6.28), that for anti-β2-glycoprotein 1 antibodies (anti-β2GP1) was 1.87 (95% CI 1.00-3.49) and that for anti-cytoplasmic neutrophil antibodies (ANCAs) was 9.56 (95% CI 3.16-28.91). A total of 9 studies determined ANAs were various widely in their qualities, and there was considerable heterogeneity in the results of meta-analysis. Subgroup analysis failed to demonstrate a statistical significance in any of the subgroups considered ( P >0.05). Egger’s test showed that there was no publication bias. Conclusions: This study suggested that there was a higher seroprevalence of autoantibodies (including ANAs, ACAs, anti-β2GP1 and ANCAs) in COVID-19 patients compared to control subjects and identified a possible association between SARS-CoV2 and autoantibodies positivity.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.054 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".