Epidemiologic approaches to infection and immunity: the case of reactive arthritis
Bibliographic record
Abstract
PURPOSE OF REVIEW: There is significant evidence that infection and arthritis are linked, but the nature of this association is unclear. The goal of this review is to examine the case of reactive arthritis (ReA), an inflammatory arthritis with a clear infectious trigger. We will first examine the current state of knowledge of ReA epidemiology and follow it with a discussion of the epidemiologic challenges that ReA studies face. RECENT FINDINGS: Recent studies have examined outbreaks of gastroenteritis to try and elucidate the epidemiology of ReA. Some have found higher levels of self-reported arthritis than previously thought, and others have implicated organisms such as Escherichia coli O157:H7 that were not traditionally associated with ReA. There is also evidence that the severity of initial infection may be associated with a higher relative risk of developing ReA. New population-based studies have further clarified the natural history of infection and subsequent ReA, demonstrating the power of community surveillance. Despite these findings, several methodological issues complicate the study of ReA. Problems include lack of standard diagnostic criteria, varying culture methods, selection bias and difficulties in establishing a control population. SUMMARY: Recent studies have continued to increase our knowledge of the epidemiology of ReA. Addressing the multiple challenges that face the study of infection and arthritis will be very useful for future study.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".