Bibliographic record
Abstract
Diagnosis of psoriatic arthritis (PsA) is complex because not all patients with psoriasis and musculoskeletal symptoms of pain, stiffness, and dysfunction have PsA. Instead, they may have other inflammatory conditions such as rheumatoid arthritis, gout, or septic arthritis, or noninflammatory conditions such as osteoarthritis, recurrent tendonitis, mechanical back pain, or a myriad other musculoskeletal conditions. To acquire skill in diagnosing and monitoring the disease course of PsA, a clinician must recognize that there are multiple clinical domains that may be affected, including peripheral joints, entheseal insertion sites, dactylitis, and the spine. They must also appreciate the clinical features (history and physical examination) that are characteristic of immunologic inflammation and know how to utilize and interpret laboratory and imaging studies. Rheumatologists are expected to be skilled in these assessments. It is also helpful for dermatologists, primary care physicians, and other clinicians who work with psoriasis patients to have a working knowledge of assessments in PsA in order to identify and triage the patient for optimal management. Features that assist identification and assessment of PsA are reviewed in this article.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".