Patients Considered as Having Undifferentiated Peripheral Inflammatory Arthritis: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To systematically review the differential diagnosis and minimal clinical investigation used prior to making a diagnosis of undifferentiated peripheral inflammatory arthritis (UPIA). METHODS: A systematic literature search was performed for articles published between January 1950 and December 2008 in Medline and Embase, and for abstracts presented at the 2007 and 2008 meetings of the American College of Rheumatology (ACR) and European League Against Rheumatism (EULAR). Studies including defined cohorts of patients with UPIA were retrieved according to predefined inclusion/exclusion criteria. Selected studies were systematically reviewed and relevant data extracted. Baseline characteristics were also recorded to obtain a clinical picture of patients classified as UPIA. RESULTS: Seventy-four articles were included. Of those, 52 reported baseline characteristics. Tremendous variation existed among studies, reflecting the different inclusion/exclusion criteria used. Rheumatoid arthritis, spondyloarthropathies, osteoarthritis, crystal arthritis, connective tissue diseases, and infections were the most common diagnoses of exclusion for UPIA and made up the other subsets of patients in cohorts with mixed populations. The baseline investigation undertaken prior to diagnosis of UPIA was reported in 7 articles. History, physical examination, tender and swollen joint count, rheumatoid factor, HLA-B27, erythrocyte sedimentation rate, C-reactive protein, and radiographs of hands and feet were the only items mentioned in at least 50% of the reports. CONCLUSION: Studies of UPIA are heterogeneous. Few studies reported on the minimal clinical investigation necessary to arrive at a diagnosis of UPIA. Differential diagnosis usually consisted of the most common rheumatologic conditions but could be vast.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".