Further Evidence for Influenza and Pneumococcal Vaccination in Patients Treated with Disease Modifying Antirheumatic Drugs and Anti-Tumor Necrosis Factor Agents
Bibliographic record
Abstract
Patients with rheumatoid arthritis (RA) have increased risk of infections including possible vaccine-preventable ones1,2,3,4,5,6,7. Susceptibility to infections is considered to be partly associated with RA itself, i.e., immunological alterations as a part of the disease, disease activity, and disability2,3. Antirheumatic treatment, in particular longterm use of glucocorticoids, but also traditional disease modifying antirheumatic drugs (DMARD) and biological remedies have all been shown to increase risk of infections4,5,6. In addition, higher age, concomitant smoking, common comorbidities such as chronic obstructive pulmonary disease, diabetes mellitus, kidney disease, or malignancies contribute to increased infection risk2,3,5. Vaccination is an appealing strategy in the attempt to reduce the burden of infectious diseases in RA. The recent European League Against Rheumatism (EULAR) recommendations, based on current evidence and expert opinion, recommend that rheumatologists strongly consider annual inactive influenza and pneumococcal vaccination for their patients with inflammatory rheumatic diseases7. Similarly, the US Centers for Disease Control (CDC) Advisory Committee on Immunization Practices (ACIP) recommends these vaccines to all subjects ≥ 65 years and those treated with immunosuppressive agents8. Thus, the majority of patients with RA should get annual seasonal influenza vaccine, and pneumococcal vaccine at least once in life. However, vaccine coverage among patients with RA, including those treated with anti-tumor necrosis factor (TNF) agents is still low7,8. There are a number of reasons for low influenza and pneumococcal vaccination rates. Anti-TNF treatments as first biologics for treatment of RA have been available for some 15 years, so data on safety, immunogenicity, and effectiveness of vaccines performed under treatment with these and other biologics are still limited. Prelicensure studies … Address correspondence to Dr. Kapetanovic, E-mail: meliha.c_kapetanovic{at}med.lu.se
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.002 |
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".