Outcome Measures for the Evaluation of Treatment Response in Hidradenitis Suppurativa for Clinical Practice
Bibliographic record
Abstract
Importance: Although several clinician- and patient-reported outcome measures have been developed for trials in hidradenitis suppurativa (HS), there is currently no consensus on which measures are best suited for use in clinical practice. Identifying validated and feasible measures applicable to the practice setting has the potential to optimize treatment strategies and generate generalizable evidence that may inform treatment guidelines. Objective: To establish consensus on a core set of clinician- and patient-reported outcome measures recommended for use in clinical practice and to establish the appropriate interval within which these measures should be applied. Evidence Review: Clinician- and patient-reported HS measures and studies describing their psychometric properties were identified through literature reviews. Identified measures comprised an item reduction survey and subsequent electronic Delphi (e-Delphi) consensus rounds. In each consensus round, a summary of outcome measure components and scoring methods was provided to participants. Experts were provided with feasibility characteristics of clinician measures to aid selection. Consensus was achieved if at least 67% of respondents agreed with use of a measure in clinical practice. Findings: Among HS experts, response rates for item reduction, e-Delphi round 1, and e-Delphi round 2 surveys were 76.4% (42 of 55), 90.5% (38 of 42), and 92.9% (39 of 42), respectively; among patient research partners (PRPs), response rates were 70.8% (17 of 24), 100% (17 of 17), and 82.4% (14 of 17), respectively. The majority of experts across rounds were practicing dermatologists with 18 to 19 years of clinical experience. In the final e-Delphi round, most PRPs were female (12 [85.7%] vs 2 males [11.8%]) and aged 30 to 49 years. In the final e-Delphi round, HS experts and PRPs agreed with the use of the HS Investigator Global Assessment (28 [71.8%]) and HS Quality of Life score (13 [92.9%]), respectively. The most expert-preferred assessment interval in which to apply these measures was 3 months (27 [69.2%]). Conclusions and Relevance: An international group of HS experts and PRPs achieved consensus on a core set of HS measures suitable for use in clinical practice. Consistent use of these measures may lead to more accurate assessments of HS disease activity and life outcomes, facilitating shared treatment decision-making in the practice setting.
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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 | 0.110 | 0.309 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".