Telemedicine in the Care of Patients with Atopic Dermatitis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract: Importance: More patients are opting for telemedicine because of its convenience and potential cost savings, especially post-COVID-19. This is also applicable to atopic dermatitis (AD). Objective: To investigate telemedicine’s impact on the care of patients with AD. Methods: Relevant articles from five databases—namely, MEDLINE, Embase, Scopus, CINAHL, and the Cochrane Library—were searched up to August 1, 2023. Controlled prospective clinical trials, long-term follow-up studies, retrospective studies, and observational studies that assessed the effectiveness of telemedicine in terms of patient- and physician-reported outcomes of AD were included. A random-effects model was chosen to estimate all pooled data, with results presented in forest plots. Results: Regarding teleconsultation, four studies reported decreases in Patient-Oriented Eczema Measure (POEM), Investigator Global Assessment, Scoring Atopic Dermatitis, and Dermatology Life Quality Index (DLQI), which were comparable with the control arm. For electronic interventions, POEM and Hand Eczema Severity Index significantly decreased from baseline in favor of telemedicine ( P = 0.0003 and P < 0.00001, respectively), while no difference was observed for Eczema Area and Severity Index and DLQI. Conclusions: Telemedicine can be a useful adjunct to traditional face-to-face consultations in AD but with potential cost savings and convenience for patients.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.028 |
| Bibliometrics | 0.006 | 0.007 |
| 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) | 0.004 | 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".