Distinguishing Inflammatory from Noninflammatory Arthritis, Enthesitis, and Dactylitis in Psoriatic Arthritis: A Report from the GRAPPA 2010 Annual Meeting: Table 1.
Bibliographic record
Abstract
The most widely applied criteria for classifying psoriatic arthritis (PsA) are the CASPAR (ClASsification of Psoriatic ARthritis) criteria. A patient who fulfills the CASPAR criteria must have evidence of inflammatory arthritis, enthesitis, or spondylitis, and may have an inflammatory musculoskeletal component, dactylitis. Although the criteria were developed by rheumatologists, not all patients with PsA are seen by rheumatologists. Thus, it is important for clinicians such as dermatologists, primary care providers, physiatrists, and orthopedists, and patients themselves, to be able to recognize the presence of inflammatory musculoskeletal disease and distinguish it from degenerative or traumatic musculoskeletal disease. At their 2010 annual meeting, members of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) discussed the steps they are taking to define the key variables that must be present to distinguish inflammatory arthritis, enthesitis, and dactylitis from degenerative, traumatic, mechanical, or infectious forms of these conditions.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".