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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".