Hospitalizations of Patients Treated with Anti-Tumor Necrosis Factor-α Agents — A Retrospective Cohort Analysis
Bibliographic record
Abstract
OBJECTIVE: To assess the association between treatment with anti-tumor necrosis factor-α (TNF-α) agents and the occurrence of hospitalizations, their causes and complications, compared to treatment with traditional disease-modifying antirheumatic drugs in patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA), and ankylosing spondylitis (AS). METHODS: A retrospective cohort study was conducted of patients with RA, AS, and PsA treated with anti-TNF-α agents between April 2002 and December 2007. Patients were assessed during the period of anti-TNF-α treatment (Group B) and compared to an equivalent period before initiation of anti-TNF-α therapy (Group A). All hospitalization charts were reviewed and diagnoses, comorbidities, concomitant medications, and clinical course were analyzed. Statistical analysis was performed using multivariate mixed Poisson regression. RESULTS: In the study period of 57 months, 735 hospitalization events of 327 patients were analyzed. Statistically significant decreases were seen in the total number of hospitalization events as well as hospitalizations due to exacerbation of rheumatic diseases in Group B compared to Group A (44.4 vs 74.2 and 21.9 vs 47.5 per 100 patient-years, respectively; p < 0.0001). More infectious events (7.4 in Group B compared to 4.6 per 100 patient-years in Group A; p = 0.043) were associated with anti-TNF-α treatment, older age, and underlying disease, because patients with RA had higher rates of infections compared to patients with PsA and patients with AS. CONCLUSION: The overall effect of anti-TNF-α therapy was a significant decline in total hospitalization events. The decrease was more prominent in patients with RA than in patients with AS and patients with PsA, and reflected the significant decrease in hospitalizations due to rheumatic disease exacerbation. The decrease was more pronounced than the observed increase in infectious events.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".