The Value of Conventional Radiographs in Undifferentiated Arthritis: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To perform a systematic literature review on the diagnostic and predictive value of conventional radiographs (CR) in patients with undifferentiated arthritis (UA). METHODS: We performed an extended search using Medline, Embase, the Cochrane Library, and abstracts from the 2007 and 2008 meetings of the American College of Rheumatology and the European League Against Rheumatism. Articles were included based on predefined inclusion criteria, and quality was assessed by using validated quality scales. RESULTS: In total, 25 articles were included from 6003 retrieved references. Five articles described a pure UA population, 20 articles described a mixed population [mostly rheumatoid arthritis (RA) and UA]. In studies on UA, erosions on CR were strong predictors of RA diagnosis [positive likelihood ratio (LR+) 3.5-10.9; odds ratio 7.6 and 8.7). In a more heterogeneous mixed population, 20 studies reporting on 11 cohorts found a relationship between CR findings and subsequent diagnosis of RA. LR+ for erosions and/or bony decalcifications ranged from 1.8 to 9.7, and there was greater prevalence of erosions and higher Sharp-van der Heijde score in the RA group at followup. With regard to prognosis in both UA and mixed populations, an association was found between number of abnormalities on CR and poor outcome. CONCLUSION: Several studies, in pure UA and mixed populations, clearly demonstrate that CR are helpful in predicting future diagnosis of RA or worse prognosis. However, absence of abnormalities on CR does not sufficiently exclude RA or other unfavorable outcome.
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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 | 0.009 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".