Exploration of the Product of the 5-Point Investigator’s Global Assessment and Body Surface Area (IGA × BSA) as a Practical Minimal Disease Activity Goal in Patients with Moderate-to-Severe Psoriasis
Bibliographic record
Abstract
BACKGROUND/AIMS: In the treat-to-target era, psoriasis disease activity measures that can be easily performed in routine clinical practice are needed. This retrospective pooled analysis explored cutoff values of the product of the 5-point Investigator's Global Assessment and percentage of affected body surface area (IGA × BSA) correlating with achievement of minimal disease activity (MDA). METHODS: Post hoc analysis of the phase 3 clinical trials ERASURE, FIXTURE, FEATURE, and JUNCTURE was conducted to determine associations between IGA × BSA and 2 MDA definitions (Psoriasis Area and Severity Index [PASI] 90 and Dermatology Life Quality Index [DLQI] 0/1, or PASI score ≤1 or BSA <3%) in patients with moderate-to-severe psoriasis receiving secukinumab 300 mg. For each definition of MDA, a range of possible cutoff values of IGA × BSA was examined at each time point. The optimal cutoff value was determined using Youden index (YI), calculated as (sensitivity + specificity - 1). RESULTS: For MDA defined as PASI 90 and DLQI 0/1, optimal IGA × BSA cutoffs were 2.10 at week 12 (YI, 0.60; sensitivity, 0.78; specificity, 0.82), 1.02 at week 24 (YI, 0.55; sensitivity, 0.73; specificity, 0.82), and 1.00 at week 52 (YI, 0.65; sensitivity, 0.79; specificity, 0.86). For MDA defined as PASI score ≤1 or BSA <3%, optimal IGA × BSA cutoffs were 2.98 at week 12 (YI, 0.91; sensitivity, 0.99; specificity, 0.92), 2.80 at week 24 (YI, 0.94; sensitivity, 0.99; specificity, 0.95), and 3.00 at week 52 (YI, 0.96; sensitivity, 1.00; specificity, 0.96). CONCLUSION: IGA × BSA could be a valid measure highly associated with achievement of MDA.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".