Antibiotics for Treatment of Reactive Arthritis: A Systematic Review and Metaanalysis
Bibliographic record
Abstract
OBJECTIVE: To examine the efficacy and safety of antibiotic treatments for reactive arthritis (ReA). METHODS: We did a systematic review and metaanalysis of randomized controlled trials of antibiotics for treatment of ReA. We searched electronic databases and conference proceedings up to November 2011. Included trials reported on remission, joint counts, and pain or patient global scores in any language. RESULTS: Twelve trials were eligible for inclusion and 10 provided data for metaanalysis. The pooled relative risk of failure to achieve remission from a random effects model showed no significant benefit of antibiotic treatment on remission (7 trials, 375 participants, RR 0.74, 95% CI 0.49-1.10); however, substantial heterogeneity was observed (I(2) = 76.3%, p < 0.0001). The treatment effect did not differ significantly by the type of organism triggering the ReA (chlamydia, 4 trials, RR 0.80, 95% CI 0.63-1.03, vs other microorganisms, 5 trials, RR 0.72, 95% CI 0.29-1.79, metaregression p = 0.477) or use of combination antibiotics (monotherapy, 6 trials, RR 0.70, 95% CI 0.39-1.26, vs combination therapy, 1 trial, RR 0.79, 95% CI 0.63-0.99, metaregression p = 0.466). When unblinded trials were excluded, the treatment effect was attenuated and heterogeneity decreased (RR 0.87, 95% CI 0.70-1.10, I(2) = 32.8%, p = 0.19). No significant effects of antibiotic treatment were observed on joint counts, pain, or patient global scores; however, antibiotics were associated with a 97% increase in gastrointestinal adverse events. CONCLUSION: Trials of antibiotic treatment for ReA have produced heterogeneous results that may be related to differences in study design. The efficacy of antibiotics is uncertain.
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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 | 0.024 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.038 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".