Nocebo effects in systemic therapies for adult plaque psoriasis: A systematic review and meta-analysis
Bibliographic record
Abstract
Introduction The nocebo effect is defined as adverse outcomes secondary to negative patient expectations rather than the pharmacologic activity of an intervention. Nocebo effects can reduce treatment adherence and/or persistence. Therefore, nocebo effects in psoriasis need to be defined. Methods A Cochrane systematic review was updated with a search of MEDLINE, Embase, and the CENTRAL Register of Controlled Trials for phase II - IV RCTs comparing systemic therapy versus placebo for patients with moderate-to-severe plaque psoriasis. Estimates were pooled using a random effects model, and heterogeneity was evaluated using the I 2 statistic. The primary outcome was the pooled proportion of any adverse event (AE) and corresponding risk difference (RD) in patients randomized to placebo versus systemic therapy. Results A total of 103 unique trials were identified enrolling 43,189 patients. The overall pooled AE rate in patients randomized to systemic therapy was 57.1% [95% CI: 54.7–59.5%] compared to 49.8% [95% CI: 47.1–52.4%] for placebo [RD 6.7% (95% CI: 4.6–8.9%), p < 0.00001, I 2 = 75%]. Both biologic and non-biologic systemic therapy groups had a higher proportion of infectious AEs compared to placebo. No statistically significant RD in serious AEs or AEs leading to discontinuation was identified between systemic therapy and placebo groups. Discussion Half of patients exposed to inert placebo in clinical trials of systemic psoriasis therapies experienced AEs, which may be explained by nocebo effects. These findings have important implications when counseling patients and designing future studies.
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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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".