Development of Criteria to Distinguish Inflammatory from Noninflammatory Arthritis, Enthesitis, Dactylitis, and Spondylitis: A Report from the GRAPPA 2013 Annual Meeting
Bibliographic record
Abstract
OBJECTIVE: To describe a research project to develop simple clinical criteria to aid in the identification of inflammatory arthritis, enthesitis, dactylitis, and spondylitis and distinguish these from non-inflammatory conditions. The criteria are particularly intended to aid non-rheumatologists, e.g., dermatologists, who need assistance identifying psoriatic arthritis in patients with psoriasis, but may be useful to all clinicians in properly diagnosing rheumatologic conditions. METHODS: The proposed research methodology includes the use of a nominal group exercise among expert clinicians and patient focus groups, Delphi exercises among clinicians and patients, application of criteria test sets to a small group of representative patients with inflammatory and non-inflammatory musculoskeletal conditions, and validation by application of optimal criteria sets to large groups of patients with inflammatory and noninflammatory conditions. RESULTS: Examples of elements to describe inflammatory conditions derived from a nominal group exercise conducted at the 2013 GRAPPA annual meeting are described, along with planned project activities. CONCLUSION: This project will lead to the development of practical criteria to aid in the diagnosis and appropriate clinical care of patients with chronic inflammatory musculoskeletal conditions.
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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 | 0.140 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".