Influence of disease duration and socioeconomic factors on the prevalence of infection and hospitalization in rheumatoid arthritis: KRAC study
Bibliographic record
Abstract
AIM: The use of healthcare resources by rheumatoid arthritis (RA) patients can be related to the presence of disease, comorbid conditions, use of steroids, and the combined use of immunosuppressants. This study evaluated the risk factors associated with infection and hospitalization in RA. METHODS: This multicenter, cross-sectional study enrolled 3247 RA subjects fulfilling the 2010 American College of Rheumatology/European League Against Rheumatism criteria to examine the prevalence of hospitalization and episodes of documentable non-tubercular infections as a part of the "Karnataka rheumatoid arthritis comorbidity" study (KRAC). The study included 2081 subjects and 1166 were excluded due to incomplete data. Demographic, clinical and treatment variables were collected, and the events related to infections and hospitalization were extracted from the medical records. Comparative analysis and multivariate logistic regression were performed. RESULTS: Around 22% of the subjects had hospitalizations and 2.9% had infections. Infections were pertaining to dental (1.3%), urinary tract (1.6%) and candidiasis (0.2%). Skin- and soft tissue-related infections were found in 1.8% and 0.3% of patients, respectively. Increased need of hospitalization in RA patients was associated with advanced age (≥60 years), lower education, family income, and longer duration of RA. Presence of comorbidity, usage of three or more disease-modifying anti-rheumatic drugs (DMARDs) and family income influenced the likelihood of infection. Dental infections were less likely in working subjects and more likely in patients with increased disease duration, higher family income, comorbidities and those between the age group 40-59 years. Urinary tract infection was associated with DMARD usage. CONCLUSION: Patient-specific risk factors should be considered to improve treatment strategies and to reduce the risk of infection and hospitalization in RA patients.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".