Conjunctivitis in atopic dermatitis patients with and without dupilumab therapy – international eczema council survey and opinion
Bibliographic record
Abstract
BACKGROUND: Conjunctivitis is common in patients with atopic dermatitis (AD) in general and a commonly reported adverse event in AD clinical trials with dupilumab. OBJECTIVE: To survey opinions and experience about conjunctivitis occurring in AD, including those during dupilumab treatment in a group of AD experts from the International Eczema Council (IEC). METHODS: Electronic survey and in-person discussion of management strategies. RESULTS: Forty-six (53.5%) IEC members from 19 countries responded to the survey. Consensus was reached for several statements regarding diagnostic workup, referral and treatment. IEC members suggest that patients with AD should (i) routinely be asked about ocular complaints or symptoms, (ii) obtain information about the potential for conjunctivitis before starting dupilumab therapy and (iii) if indicated, be treated with dupilumab despite previous or current conjunctivitis. In cases of new-onset conjunctivitis, there was consensus that dupilumab treatment should be continued when possible, with appropriate referral to an ophthalmologist. LIMITATIONS: The study relies on expert opinion from dermatologists. Responses from few dermatologists without dupilumab access were not excluded from the survey. CONCLUSION: The IEC recommends that dermatologists address conjunctivitis in patients with AD, especially during treatment with dupilumab.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".