Vaccination and auto-immune rheumatic diseases
Bibliographic record
Abstract
PURPOSE OF REVIEW: To determine the safety and efficacy of adjuvant and nonadjuvant influenza A/H1NI vaccination in patients with rheumatic diseases. RECENT FINDINGS: Due to immune abnormalities and the use of steroids and immunosuppressant treatment, patients with rheumatic diseases are susceptible to infections including influenza. Infections continue to be one of the leading causes of morbidity and mortality in rheumatic diseases, partly due to the disease processes and partly due to medications. Viral infections are particularly an issue, so vaccinations would be advisable. However, because of the abnormalities in immune mechanisms in many rheumatic diseases, it is not clear whether vaccinations are well tolerated and effective. A number of studies confirmed the efficacy and safety of adjuvant and nonadjuvant influenza A/H1NI vaccination in patients with rheumatic diseases. The potential side effects associated with H1N1 vaccines were not different from those observed with seasonal influenza vaccine. The use of steroids and immunosuppressant therapies may alter the efficacy of the vaccines. Adjuvant and nonadjuvant influenza A/H1NI vaccinations have no clinically important effect on production or levels of autoantibodies in patients with rheumatic diseases. SUMMARY: H1N1 vaccination should be given to patients with rheumatic diseases.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".