Bibliographic record
Abstract
Psoriasis is a chronic, immune-mediated, inflammatory skin disease, affecting 1% to 3% of the population in Western countries. Due to advances in the understanding of psoriasis pathogenesis, in particular, the role of the interleukin-23 (IL-23)/T-helper 17 (Th17) immune axis, highly effective, targeted biologic therapies have been developed, shifting the psoriasis treatment paradigm. However, some patients do not respond or lose response to these novel therapies. Bimekizumab is a first-in-class humanized monoclonal immunoglobulin G1 (IgG1) antibody that potently and selectively inhibits both IL-17A and IL-17F, functioning as a dual inhibitor. All bimekizumab studies have shown high efficacy in psoriasis patients. Its onset of response was rapid and sustained for periods up to 60 weeks. In active-comparator trials to date, bimekizumab was superior to adalimumab (BE SURE), ustekinumab (BE VIVID) and secukinumab (BE RADIANT). It has demonstrated a consistent safety profile and high tolerability. The most common adverse events were largely restricted to mucosal candidiasis. Dual inhibition of IL-17A and IL-17F with bimekizumab showed to be a highly effective treatment for psoriasis, and the product is already approved for treatment of moderate to severe plaque psoriasis in Europe, Canada and Japan.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".