Incorporating Biologics into the Treatment of Psoriasis
Bibliographic record
Abstract
Current systemic therapies for psoriasis affect hyperkeratinization 1 or nonspecifically suppress the immune system. 2 While these therapies are effective in the treatment of psoriasis and have been in use for years, they each have specific limitations. Many current systemic therapies are associated with cumulative toxicities such as end organ damage, cancer, and teratogenesis, thus limiting their long-term use for managing psoriasis. 3 There is a significant, yet unmet need for therapy that is effective and well-tolerated without an attendant risk for systemic toxicity. 4 To help fulfill this need, the emerging generation of novel psoriasis therapies includes specific biologic agents (mainly protein-based therapies such as antibodies, fusion proteins, or recombinant cytokines) 5 that target the pathological effects of T-cells directly. 4 In some instances, these agents may be expected to induce long-lasting remissions with a favorable safety profile over currently available conventional therapies. 2 In determining the role of biologics in the treatment of psoriasis, it is necessary to understand the modes of action and potential advantages of biologics relative to the current systemic therapies. It is also appropriate to consider the profile of patients who would benefit from biologic therapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".