Patient-Reported Outcome Measures in Atopic Dermatitis and Chronic Urticaria Are Underused in Clinical Practice
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcome measures (PROMs) are validated and standardized tools that complement physician evaluations and guide treatment decisions. They are crucial for monitoring atopic dermatitis (AD) and chronic urticaria (CU) in clinical practice, but there are unmet needs and knowledge gaps regarding their use in clinical practice. OBJECCTIVE: We investigated the global real-world use of AD and CU PROMs in allergology and dermatology clinics as well as their associated local and regional networks. METHODS: Across 72 specialized allergy and dermatology centers and their local and regional networks, 2,534 physicians in 73 countries completed a 53-item questionnaire on the use of PROMs for AD and CU. RESULTS: Of 2,534 physicians, 1,308 were aware of PROMs. Of these, 14% and 15% used PROMs for AD and CU, respectively. Half of physicians who use PROMs do so only rarely or sometimes. Use of AD and CU PROM is associated with being female, younger, and a dermatologist. The Patient-Oriented Scoring Atopic Dermatitis Index and Urticaria Activity Score were the most common PROMs for AD and CU, respectively. Monitoring disease control and activity are the main drivers of the use of PROMs. Time constraints were the primary obstacle to using PROMs, followed by the impression that patients dislike PROMs. Users of AD and CU PROM would like training in selecting the proper PROM. CONCLUSIONS: Although PROMs offer several benefits, their use in routine practice is suboptimal, and physicians perceive barriers to their use. It is essential to attain higher levels of PROM implementation in accordance with national and international standards.
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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 | 0.019 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".