Development of Simple Clinical Criteria for the Definition of Inflammatory Arthritis, Enthesitis, Dactylitis, and Spondylitis: A Report from the GRAPPA 2014 Annual Meeting
Bibliographic record
Abstract
Rheumatologists are trained to determine the presence of musculoskeletal inflammation through history, physical examination, and if needed, laboratory tests and imaging. However, primary care clinicians, dermatologists, surgeons, and others who may initially see patients with musculoskeletal pain are not necessarily able to make the distinction between inflammatory (e.g., rheumatoid arthritis or psoriatic arthritis) and noninflammatory disease (osteoarthritis, traumatic or degenerative tendonitis, back pain, or fibromyalgia). If such clinicians could more readily suspect and identify possible inflammatory musculoskeletal disease, it would lead to more timely diagnosis and triage to rheumatologists for diagnosis and appropriate management. The Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) has been developing evidence-based, practical and reliable criteria that can be used by clinicians to identify inflammatory musculoskeletal disease. The research initiative involves a sequential process of expert clinician nominal group technique, patient focus groups, and Delphi exercises to identify core definitive features of inflammatory disease. The goal is to develop simple clinical criteria (history and physical examination elements) to identify inflammatory arthritis, enthesitis, dactylitis, and spondylitis and distinguish these from degenerative, mechanical, or other forms of these conditions, to achieve more timely and accurate diagnosis and referral of patients with inflammatory arthritis.
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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 | 0.050 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".