Diagnostic and Prognostic Value of Genetics in Undifferentiated Peripheral Inflammatory Arthritis: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To evaluate the diagnostic and prognostic utility of genetic testing in undifferentiated peripheral inflammatory arthritis (UPIA). METHODS: A systematic literature search was performed in Medline, Embase, the Cochrane Library, and abstracts presented at the 2007 and 2008 meetings of the American College of Rheumatology and the European League Against Rheumatism. The target studies were those evaluating diagnostic or prognostic value of genetic markers specifically in UPIA. Two reviewers independently screened titles and abstracts and reviewed included articles in detail. All data were collected using ad hoc standard forms, permitting the calculation of positive and negative likelihood ratios of each genetic marker for diagnoses of different rheumatic diseases and for the development of relevant outcomes. RESULTS: Of the 3109 articles retrieved, 26 original studies fulfilled criteria of the systematic review. The most frequent diagnosis tested was rheumatoid arthritis, followed by inflammatory polyarthritis, and spondyloarthropathies. The main prognostic outcome evaluated was development of erosions, followed by median Larsen score, remission, Health Assessment Questionnaire (HAQ) score, and persistent synovitis. In total, 122 genetic markers were tested. No genetic marker had a high likelihood ratio for the diagnosis of a specific rheumatic disease. The shared epitope was associated with poor prognosis (erosions, HAQ > 1, mortality, and persistent synovitis). Other genes did not predict outcome in undifferentiated arthritis. Other outcomes for persistent disease or disability were not studied in depth. CONCLUSION: In isolation, no studied genetic marker is very informative of a future diagnosis in patients with UPIA. The shared epitope has a slight association with poor prognosis of UPIA.
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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 | 0.010 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".