Bibliographic record
Abstract
PURPOSE OF REVIEW: Psoriasis, a chronic skin condition, characterized by scaly erythematous plaques, is prevalent in around 2% of the population. Around 25% of psoriasis patients have psoriatic arthritis (PsA), an inflammatory musculoskeletal disease that often leads to progressive joint damage and disability. Psoriatic diseases (PsD) encompassing psoriasis and PsA, are often associated with pathophysiologically related conditions like uveitis and inflammatory bowel disease as well as comorbidities such as cardiovascular disease. Due to the heterogeneous nature of PsD, diagnosis and treatment is a challenge. Biomarkers can objectively measure variables, such as disease state, disease progress, and treatment outcomes, thus offering the possibility for better management of disease. This review focuses on some of the biomarker research that was carried out in PsD in the past year. RECENT FINDINGS: Diverse biomarker types ranging from SNPs, mRNA, proteins, metabolites and immune cell profiles have been categorized as per the Biomarkers, EndpointS and other Tools (BEST) resource developed by the FDA/NIH. Some of the latest research has focused on multiomic assays and these along with advanced bioinformatic tools can help in better disease management. SUMMARY: Recent developments in PsA biomarker research show promise in identifying markers that can help in diagnosis, assess disease activity and predict treatment response. However, most studies are in the early discovery and verification state. Large-scale studies to replicate findings and develop and validate predictive algorithms are required.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".