Greater Efficacy with Secukinumab Treatment is Associated with Greater Psoriasis Symptom Relief: Results from Secukinumab Clinical Trial Data
Bibliographic record
Abstract
Background Psoriasis negatively affects patients’ quality of life. Secukinumab is a human interleukin-17A antagonist indicated for the treatment of moderate-to-severe plaque psoriasis. Objectives The current analysis evaluated the benefits of secukinumab by assessing relationships between disease severity and patient-reported symptoms. Methods Correlations between psoriasis-related itching, pain, and scaling and disease severity scores (Psoriasis Area and Severity Index [PASI] and Investigator's Global Assessment [IGA]) were evaluated at baseline, Week 12, and change from baseline to Week 12 using secukinumab clinical data from ERASURE and FIXTURE. Symptom responder status and PASI/IGA change were evaluated using logistic modeling. Results Correlation coefficients ranged 0.11-0.49 for PASI and 0.19-0.52 for IGA. Greater PASI response was related to greater symptom response/complete relief. Conclusions Results further demonstrate the relationship between traditional clinical measures of disease severity and patient-reported, psoriasis-related itching, pain, and scaling –- hence the need to consider both outcomes together to evaluate treatment effects in this disease fully.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".