Advancing research paradigms and pathophysiological pathways in psoriatic arthritis and ankylosing spondylitis: Proceedings of the 2017 Platform for the Exchange of Expertise and Research (PEER) meeting
Bibliographic record
Abstract
The seronegative spondyloarthropathies, including psoriatic arthritis (PsA) and ankylosing spondylitis (AS), are characterized by varied clinical symptoms, severity, and disease course [1], [2]. Diagnosis and monitoring can be challenging because there is no definitive laboratory biomarker for reliably measuring inflammation or other disease processes associated with spondyloarthropathies. Over time, many patients with PsA and AS eventually experience significant disability and impaired quality of life [1], [2]. This may be partially accounted for by delays in diagnosis and subsequent treatment [3], as well as the presence of comorbidities. In recent years, research efforts aimed at identifying risk factors for PsA, including clinical, imaging, genetic, and laboratory assessments, have yielded major advances. The Platform for the Exchange of Expertise and Research (PEER) was formed to facilitate the exchange of research insights, sharing of expertise, and discussion of unmet needs in rheumatology research. The objective of the current report is to provide an overview of the 2017 PEER meeting, which was held on May 19–20, 2017, in London, UK, and highlighted the most up-to-date research findings regarding PsA and AS pathophysiology, early detection, comorbidities, and treatment.
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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 | 0.045 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".