Trends in Serious Infections in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To examine trends in the rates of serious infections among patients diagnosed with rheumatoid arthritis (RA) in 1995-2007 compared to rates previously reported from the same geographical area diagnosed 1955-1994. METHODS: A population-based inception cohort of patients with RA in 1995-2007 was assembled and followed through their complete medical records until death, migration, or December 31, 2008. All serious infections (requiring hospitalization or intravenous antibiotics) were recorded. Person-year (py) methods were used to compare rates of infection. RESULTS: Among 464 patients with incident RA in 1995-2007, 54 had ≥ 1 serious infection (178 total). These were compared to 609 patients with incident RA in 1955-1994 (290 experienced ≥ 1 serious infection; 740 total). The rate of serious infections declined from 9.6 per 100 py in the 1955-1994 cohort to 6.6 per 100 py in the 1995-2007 cohort. Serious gastrointestinal (GI) infection rates increased from 0.5 per 100 py in the 1955-1994 cohort to 1.25 per 100 py in the 1995-2007 cohort. Among patients with a history of serious infection, the rate of subsequent infection increased from 16.5 per 100 py in 1955-1994 to 37.4 per 100 py in 1995-2007. There was an increase in the rate of serious infections in patients who received biologic agents, but this did not reach significance. CONCLUSION: Aside from GI infections, the rate of serious infections in patients with RA has declined in recent years. However, the rate of subsequent infections was higher in recent years than previously reported.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".