Teledermatology Versus In-Person Visits for the Follow-Up of Atopic Dermatitis Patients
Bibliographic record
Abstract
BACKGROUND: In recent years, teledermatology has rapidly emerged as a healthcare delivery method with potential implications for managing chronic inflammatory dermatoses like atopic dermatitis (AD). OBJECTIVES: This study assesses the utility of telemedicine in the management of AD by comparing virtual care with traditional in-office visits with the aim of identifying differences in clinical outcomes between these 2 healthcare delivery modalities. METHODS: Patients of all ages with AD were recruited from 2 dermatology practices. Consecutive patients presenting to the clinics who met the inclusion criteria were invited to enrol in the study. Those who consented to participate were randomly assigned to the virtual or in-person arm of the study, with the opportunity to decline care in either study arm. The inclusion criteria required participants to have a confirmed diagnosis of AD. Exclusion criteria included significant comorbidity that might affect the course of treatment, inaccessibility to teleconsults such as not having a camera for video conferences, and self-declared limitations in operating Zoom. Patients were assessed at baseline (week 0), 4 to 6 weeks, and 8 to 12 weeks using 6 efficacy parameters. RESULTS: In the virtual group, all 6 dermatological measures suggested improved outcomes. Average Body Surface Area scores decreased (β = -.07, 95% CI = -0.1, -0.3) over the course of follow-up. Virtual care patients had 80% lower odds of moderate-to-severe uncontrolled disease (OR = 0.2; 95% CI = 0.06, 0.5) and pruritus (OR = 0.2, 95% CI = 0.05, 0.7) over time. CONCLUSIONS: This study supports teledermatology as a feasible and effective option for providing follow-up care for atopic dermatitis patients of various demographic standings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".