Bibliographic record
Abstract
Psoriatic arthritis (PsA) is a systemic inflammatory condition characterized by musculoskeletal involvement associated with cutaneous psoriasis. The PsA clinical picture is generally dominated by peripheral arthritis with or without inflammatory spinal disease, as well as enthesitis and dactylitis1. Extraarticular features belonging to the spectrum of spondyloarthropathies are also frequently observed, such as uveitis and gut inflammation2. PsA’s heterogeneous clinical spectrum is reflected in its complex pathogenesis, where genes and environmental triggers interfere with the innate and acquired immune system3,4,5,6. On this basis, it is not surprising that the definition of the key domains and instruments for the evaluation of PsA has been, and remains, challenging. The present classification of domains is based on the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) & Outcome Measures in Rheumatology (OMERACT) initiative, which proposed a framework for the long list of possible domains7,8. Three categories of domains of interest were considered: “inner core,” “outer … Address correspondence to Dr. A. Cauli, Department of Medical Sciences, Policlinico of the University of Cagliari, ss 554 Monserrato, 09042 Italy; E-mail: cauli{at}medicina.unica.it
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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".