704 - Effectiveness of atopic dermatitis patient education programs – a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Patient education in atopic dermatitis (AD) has worked in parallel to the gold standard of pharmacological treatment as a foundational component of therapeutic regimens. In addition to improving patient education, past investigations of educational interventions have demonstrated profound reductions in disease severity for patients living with AD. However, prior meta-analytical work has focused mostly on specific patient groups (e.g pediatric education), and there is limited data on the impact of said educational interventions across different AD severity scales, adult populations, and virtual platforms. Objective In this study, we conducted a systematic review of the literature to determine the effectiveness of online programming in AD education compared to in-person interventions. Methods A comprehensive search was conducted in accordance with the Cochrane Handbook for Systematic Reviews of Interventions 2019. Studies were retrieved based on articles published up to 04 April 2023. Adherence to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) Statement guided the reportage process for this systematic review and meta-analysis. The primary outcome of our meta-analysis was the effect of various educational modalities on atopic dermatitis severity as measured by multiple scales across the studies, the most common including SCORAD, Dermatology Life Quality Index (DLQI), Patient Oriented Eczema Measure (POEM), and Eczema Area and Severity Index (EASI). Results Our study found significant differences in various disease severity and patient-reported indexes for those enrolled in educational experimental compared to control including SCORAD (standard mean difference [SMD] = 0.65, 95% Confidence Intervals (CI): 0.45–0.86), P < 0.00001), QOL (SMD= 0.36 ,95% CI: 0.18–0.54, P = 0.0002), POEM (SMD = 0.28, 95% CI: 0.07–0.49, P = 0.009), and EASI (SMD = 0.80, 95% CI: 0.54–1.06, P < 0.00001). Conclusion Our pooled analyses showed that targeted educational programs are an effective method of improving quality of life and reducing disease severity in AD. Moreover, virtual interventions can be employed as constructive tools for reducing barriers of access to patient education. Future research on educational interventions should utilize various methodologies to encourage individual learning preferences with a focus on adult cohorts.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.047 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".