Bibliographic record
Abstract
Psoriasis is a chronic, proliferative, and inflammatory skin disease affecting 2-3% of the population and is characterized by red plaques with white scales. Psoriasis is a disease that can affect many aspects of professional and social life. Currently, several treatments are available to help control psoriasis such as methotrexate, ciclosporin, and oral retinoids. However, the available treatments are only able to relieve the symptoms and lives of individuals. The discovery of new immunological factors and a better understanding of psoriasis have turned to the use of immunological pathways and could develop new biological drugs against specific immunological elements that cause psoriasis. Biological drugs are less toxic to the body and more effective than traditional therapies. Thus, they should improve the quality of life of patients with psoriasis. This review describes new psoriasis treatments, which are on the market or currently in clinical trials that are being used to treat moderate-to-severe plaque psoriasis. In addition, this paper describes the characteristics and mechanisms in detail. In general, biological drugs are well tolerated and appear to be an effective alternative to conventional therapies. However, their effectiveness and long-term side effects need to be further researched.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".