HLA-Cw6 status predicts efficacy of biologic treatments in psoriasis patients
Bibliographic record
Abstract
Over the past decade, biologic therapies have been developed to treat auto-inflammatory conditions such as psoriasis.They have the advantage of better target specificity than traditional systemics such as methotrexate and cyclosporine and therefore significantly reduce side-effects and toxicity associated with wide spread systemic treatments.It has been suggested that the efficacy of biologics used in the treatment of psoriasis may be related with HLA-Cw6 status.Using HLA-Cw6 as a biomarker would therefore provide an advantage in the selection of a biologic agent for successful treatment based on a patient's genetic makeup and thus allowing us to use HLA-Cw6 to individualize therapy for patients with moderate-to-severe psoriasis.In the present study, the HLA-Cw6 status was determined for psoriasis patients previously treated with etanercept, adalimumab, efalizumab, infliximab or ustekinumab.The success or failure rates of the biologic treatments were compared for patients with and without the HLA-Cw6 allele.The HLA-Cw6 status was significantly associated to the treatment outcomes for biologics efalizumab (no longer on the market), infliximab and ustekinumab; but not etanercept or adalimumab.These results support the use of HLA-Cw6 status as a biomarker for biologic treatment in moderate-to-severe psoriasis 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".