Effective and sustainable biologic treatment of psoriasis: what can we learn from new clinical data?
Bibliographic record
Abstract
The introduction of the biologic agents, adalimumab, etanercept, infliximab and ustekinumab, has provided more options for the short- and long-term treatment of patients with psoriasis. Physicians are now able to achieve and maintain effective disease control in more patients using biologic therapies. Newly published clinical data support the introduction of novel optimization strategies to further improve outcomes in patients with psoriasis. Recent randomized controlled clinical trials have provided data on the efficacy of conventional therapies, including systemic agents, and biologics at specific time points. Switching from methotrexate to a tumour necrosis factor (TNF)-α antagonist after 16 weeks can improve response rates, as demonstrated in a study of patients with moderate-to-severe psoriasis, while the benefit of long-term methotrexate use remains unclear. In a separate study, psoriasis area and severity index (PASI) ≥ 75 response rates were maintained over time (>3 years for adalimumab), suggesting that long-term biologic therapy is an effective and sustainable treatment option for psoriasis. For each individual patient, the benefit of a particular treatment needs to be balanced with the risks. The lack of head-to-head trials of antipsoriatic therapies, particularly biologic therapies, does not help with making individualized treatment decisions. However, a benefit-risk assessment of TNF-α antagonists calculated from an integrated analysis of published literature in moderate-to-severe psoriasis can be used to aid clinical practice. The number needed to treat, number needed to harm and number of patient years of observation to detect an adverse event have been determined for adalimumab, etanercept and infliximab. The benefit-risk profiles generated demonstrated that, during the initial year of treatment, likelihood of success with TNF-α antagonists was several orders of magnitude greater than the likelihood of serious toxicity.
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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 | 0.063 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.014 | 0.038 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".