Relative Clinical Influence of Clinical, Laboratory, and Radiological Investigations in Early Arthritis on the Diagnosis of Rheumatoid Arthritis. Data from the French Early Arthritis Cohort ESPOIR
Bibliographic record
Abstract
OBJECTIVE: To evaluate the relative level of influence of usual investigations in early arthritis on the diagnosis of rheumatoid arthritis (RA). PATIENTS: those included in the ESPOIR early arthritis cohort, a national cohort of patients with grade ≥ 2 synovitis for > 6 weeks and < 6 months. The diagnostic properties of variables assessed at baseline were measured against the diagnosis of RA defined by American College of Rheumatology criteria (at any timepoint between inclusion and 12-month followup) and expert opinion. Various models, including (1) clinical data; (2) clinical + radiographic data (plain radiographs); (3) addition of rheumatoid factor (RF) and/or anti-cyclic citrullinated peptide (anti-CCP); and (4) addition of HLA-DR typing, were assessed by comparing areas under the curves for ROC curves. RESULTS: Of 731 patients studied, 372 (50.9%) satisfied criteria for RA at 1 year. In univariate analysis, sensitivity was highest for distal articular presentation (94.6%), presence of IgM RF (69.4%), pain on metatarso-phalangeal squeeze test (66.1%), and presence of anti-CCP (65.6%); whereas specificity was highest for nodules (100%), HLA typing: shared-epitope double dose (95.9%), radiographic erosions (86.5%), and anti-CCP antibodies (86.4%). The most efficient model included swollen joint count, morning stiffness, erosions, RF, and anti-CCP. Adding rheumatoid nodules, C-reactive protein, or HLA-DR information was not contributive. CONCLUSION: In addition to the clinical variables and radiographs, RF and/or anti-CCP are the single variables of interest that are contributive for the diagnosis of RA.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".