Bibliographic record
Abstract
New paradigms in the management of psoriatic arthritis (PsA) are gaining great acceptance in the rheumatology community, including early treatment1, remission as a treatment objective2, assessment of all domains involved3, and frequent measuring of disease activity and adjusting therapy accordingly (treat to target)4. To achieve these goals, we need effective therapies. The introduction of biologic therapies, mainly tumor necrosis factor inhibitors (TNFi), has greatly improved our ability to treat the various manifestations of PsA. These various manifestations include peripheral and axial joint, skin, and nail involvement; enthesitis and dactylitis being among the more frequent ones. It is important to gather data for all these clinical features to assess disease activity. Remission criteria and activity indexes borrowed from rheumatoid arthritis (RA) have been used in PsA, but they clearly are unable to include all PsA manifestations2,5,6. Composite measures combine several dimensions of disease status, often by combining these different domains into a single score. Such indices seem to be more efficient than unidimensional instruments2,5,6. Composite measures, however, give rise to some concerns because a single measure that encompasses diverse domains might lose the ability to differentiate between activity in individual domains. At the OMERACT meeting (Outcome … Address correspondence to Dr. E.R. Soriano, Hospital Italiano de Buenos Aires, Sección Reumatología, Servicio de Clínica Médica, Gascon 450, Buenos Aires 1181, Argentina. E-mail: enrique.soriano{at}hospitalitaliano.org.ar
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 | 0.038 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.022 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 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".