Smoking does not Alter the Therapy Response to Systemic Anti-psoriatic Therapies: A Two-country, Multi-centre, Prospective, Non-interventional Study
Bibliographic record
Abstract
Psoriasis can involve the skin, joints, nails and cardiovascular system and result in a significant impairment in quality of life. Studies have shown a lower response rate to systemic anti-psoriatic therapies in smokers, and smoking is a trigger factor for psoriasis. The aim of this study was therefore to analyse the response to systemic therapies for psoriasis, with a focus on smoking. Prospectively collected data from patients with moderate to severe psoriasis included in the national psoriasis registries for Germany and Switzerland (PsoBest and SDNTT) were analysed. Therapy response was defined as reaching a Psoriasis Area and Severity Index (PASI) reduction of 75%, PASI ≤ 3 or Dermatology Life Quality Index (DLQI) ≤ 1. Out of 5,346 patients included in these registries, 1,264 met the inclusion criteria for this study. In the smoking group, 715 (60.6%) reached therapy response at month 3, compared with 358 (63.7%) in the non-smoking group (p ≤ 0.269), 659 (74.1%) vs. 330 (77%) reached therapy response at month 6 (p ≤ 0.097), and 504 (76.6%) vs. 272 (79.0%) at month 12 (p ≤ 0.611). Therefore, these data do not show that smoking affects the response rate of anti-psoriatic therapy after 3, 6 and 12 months.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".