Harmonizing epidemiological research methodology for atopic dermatitis research: protocol for the EPISTAR international consensus exercise
Bibliographic record
Abstract
Background: Epidemiological studies of atopic dermatitis lack standardization in key areas, including how the burden is collected and reported, diagnostic criteria, sociodemographic factors and measurement of disease severity. Therefore, direct cross-study comparisons, recognition of population differences and pooled analyses are challenging or not possible. Consequently, the burden of atopic dermatitis remains difficult to assess and address. The Epidemiological Study Designs for Atopic Dermatitis Research (EPISTAR) initiative aims to reach consensus on which domain items should be recommended for future population-based epidemiological studies on atopic dermatitis and how they should be assessed. Methods: In phase 1, a steering group consisting of experts from dermatology and epidemiology as well as patient representatives will generate an initial list of items constituting key variables to measure. This list will be created by reviewing the existing literature, prioritizing evidence from systematic reviews where available. Phase 2 will include an international consensus exercise, conducted through eDelphi methodology. Phase 3 will involve an online consensus conference. In each Delphi round, international participants from diverse stakeholder groups will be invited to assess each item by rating their level of agreement with the item and methods by which it can be measured. Items that reach consensus will be removed after each round. Data analysis will follow predefined consensus criteria, with raw numbers, means and frequencies reported. Discussion: This harmonized approach has the potential to transform the field of atopic dermatitis epidemiology by addressing gaps in data quality and comparability, facilitating meta-analyses, and ultimately informing evidence-based policy and clinical guidelines.
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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 | 0.239 | 0.228 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.086 | 0.029 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".