Bibliographic record
Abstract
This article reviews recent advances in psoriatic arthritis (PsA) over the past several years with emphasis on early diagnosis, better understanding of pathogenesis, and new therapeutic approaches. Early diagnosis is important, since people who present late do not fare as well. There are a number of clinical, laboratory, and ultrasound features that can help identify patients destined to develop PsA, and several screening tools have been developed. It is recognized that genetic and epigenetic factors, as well as T cells and cytokines, play a role in the pathogenesis of PsA, and several targets have been identified for therapeutic interventions. New therapies have been developed and tested in PsA and have been found to be highly effective for both skin and joint manifestations of the disease. The expectation is that, in the future, PsA patients will be treated early and more aggressively and that there will not be significant progression of joint damage. Moreover, with effective treatment of the skin and joint disease and management of risk factors for the comorbidities, we can expect to reduce their occurrence and further reduce the excess mortality and reduced quality of life and function in these patients.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".