Standardization of Lesion Classification and Assessment by Investigators in Clinical Trials for Hidradenitis Suppurativa
Bibliographic record
Abstract
Importance: Accurate classification and reliability in assessment for lesions of hidradenitis suppurativa (HS) by investigators is critical to the determination of responder status and to overall data quality in clinical trials. Objective: To establish consensus-based morphological definitions of HS lesions and guidance statements that standardize investigator lesion assessments for implementation in clinical trials. Evidence Review: Health professionals (primarily dermatologists) with expertise in the measurement of HS disease activity as well as novice raters completed a preliminary questionnaire in which participants were asked to assess images of HS lesions and provide qualitative feedback on their decision making. Based on this feedback, detailed morphologic definitions for lesions and guidance statements that standardize lesion assessments were formulated and presented for consensus voting in 2 electronic Delphi surveys. A virtual group discussion after round 1 supported participants in round 2 voting. Findings: Response rates were 84.7% (50 of 59), 86.0% (43 of 50), and 90.9% (40 of 44) in the preliminary, electronic Delphi round 1, and electronic Delphi round 2 surveys, respectively. Morphological definitions for 11 lesion types achieved the prespecified 70% consensus threshold, with 9 definitions reaching at least 90% agreement. After 2 electronic Delphi rounds, 16 of 18 guidance statements achieved the prespecified consensus threshold, with 13 statements receiving endorsement from more than 80% of participants. Two guidance statements related to assessment of tunneled plaques with multiple openings and assessment of scalp lesions failed to reach consensus. Conclusions and Relevance: Common morphologic definitions and guidance that standardize assessment of HS lesions can be implemented in clinical trial protocols and investigator trainings with the goals of improving accuracy and reliability of investigator ratings.
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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 | 0.831 | 0.863 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".