Anti-SARS-CoV-2 Spike Antibody Titers and Neutralizing Antibodies in Vaccinated Rheumatoid Arthritis Patients
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A serological test is used to assess the efficacy of vaccination. It has been reported that anti-SARS-CoV-2 spike (S) and neutralizing antibody (Ab) levels are lower following vaccination in patients with rheumatic disease. Here, we investigated anti-SARS-CoV-2 S and neutralizing Abs in vaccinated rheumatoid arthritis (RA) patients in Japan. Anti-SARS-CoV-2 S and neutralizing Abs were quantified in 101 RA patients and 117 controls. Anti-SARS-CoV-2 S Ab levels were lower in RA patients than both earlier after vaccination in controls (mean RA 324.1 ± 591.8 SDM vs. control 1216.6 ± 854.4 [U/mL], p < 0.0001) and later after vaccination (324.1 ± 591.8 vs. 582.0 ± 415.6 [U/mL], p = 0.0002). The interval between vaccination of the RA patients and serum collection was longer than for controls early after vaccination (142.1 ± 31.6 vs. 98.3 ± 11.2 [days], p < 0.0001), but shorter than the later sample from the controls (142.1 ± 31.6 vs. 257.3 ± 11.2 [days], p < 0.0001). Importantly, anti-SARS-CoV-2 neutralizing Ab titers in RA patients were higher than in either early or later control samples (10.7 ± 4.9 vs. 8.6 ± 6.6 [%], p = 0.0072, and 10.7 ± 4.9 vs. 3.1 ± 3.7 [%], p < 0.0001, respectively). Anti-SARS-CoV-2 S Ab titers in vaccinated RA patients were lower than in controls, but they were influenced by other clinical manifestations. Anti-SARS-CoV-2 neutralizing Ab levels were independently increased in RA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".