The 5-point Investigator’s Global Assessment (IGA) Scale: A modified tool for evaluating plaque psoriasis severity in clinical trials
Bibliographic record
Abstract
BACKGROUND: To evaluate new psoriasis treatments, clinicians, regulators and pharmaceutical developers require well-accepted, clinically meaningful measures of disease severity. The Psoriasis Area and Severity Index (PASI) score is most widely used as a primary endpoint in clinical trials, although it is not routinely used in clinical practice. OBJECTIVE: Characterize a 5-point Investigator's Global Assessment (IGA) tool and evaluate whether it meets the needs for a valid, clinically meaningful measure. METHODS: A 5-point IGA tool was developed with input from regulatory authorities and clinical trial investigators involved with psoriasis drug development and evaluation. Associations between IGA 0/1 responder rates and PASI scores were evaluated using data from two phase 2 studies with the anti-interleukin (IL)-17A monoclonal antibody secukinumab (AIN457) that utilized a similar 6-point IGA. RESULTS: The 5-point IGA has a more stringent definition for a score of 1 ("almost clear") compared with 6-point IGA/Physician's Global Assessment (PGA) tools used in previous trials of other biologics in moderate-to-severe psoriasis. Whereas IGA/PGA 0/1 responder rates for earlier scales are strongly associated with PASI 75, the IGA 0/1 rate for the secukinumab 6-point scale was more robust, demonstrating a strong association with PASI 90, and the results for the 5-point IGA are expected to show the same association. DISCUSSION: The 5-point IGA is a valid measure of disease severity and meets the need for a clinically meaningful measure of success for psoriasis treatment 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 | 0.174 | 0.192 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".