Therapeutic education in atopic dermatitis: A position paper from the International Eczema Council
Bibliographic record
Abstract
BACKGROUND: Atopic dermatitis (AD) is a chronic, inflammatory skin disease that affects as many as 12.5% of children aged 0-17 years and 3% of the adult population. In the United States, 31.6 million children and adults are estimated to be living with AD. OBJECTIVE: Therapeutic patient education (TPE) has proven its value in the management of chronic diseases for which adherence to therapy is suboptimal. This article explores experts' opinions and treatment practices to determine if TPE is a recommended and effective method for treating AD. METHODS: Forty-two (51%) of 82 Councilors and Associates of the International Eczema Council (IEC), an international group with expertise in AD, responded to an electronic survey on TPE and AD. RESULTS: Most respondents (97.5%) agreed that TPE should play an important role in the management of AD. Many respondents (82.9%) believed that all patients with AD, regardless of disease severity, could benefit from TPE. LIMITATIONS: The International Eczema Council survey lacks specific information on AD severity. CONCLUSIONS: Publications have shown the positive effect of TPE on the course of the disease, the prevention of complications, and the autonomy and quality of patient life. Survey respondents agreed that TPE can improve the quality of patient care and patient satisfaction with care.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.019 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".