Teledermatology in Atopic Dermatitis: A Systematic Review
Bibliographic record
Abstract
Telemedicine use has been increasing especially during the COVID-19 pandemic. Various studies have outlined benefits of telemedicine including improving health equity, reducing wait times, and cost-effectiveness. Skin diseases such as atopic dermatitis (AD) may potentially be managed via telemedicine. However, there are no evidence-based recommendations for best practices in telemedicine for assessing AD patients. The objective of this review is to assess and summarize current evidence on telemedicine modalities for AD. This review will assess patient outcomes from various telemedicine models for AD. A review protocol was developed according to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) statement. Two reviewers independently screened potential studies and extracted data. Studies were included if they evaluated any telemedicine assessment for AD. Of 2719 identified records, 5 reports were included. Two reports used the direct-access online model, 1 used web-based consultation, 1 used e-health through a personal eczema portal, and 1 used an online platform and mobile application. All models were variations of the asynchronous, store and forward model. In all the included reports, teledermatology for the follow-up of patients with AD was effective and equivalent when compared to in-person appointments or standard treatment for their respective key outcome measures. However, it is unclear what the most effective teledermatology model is due to significant heterogeneity between studies. Teledermatology may serve as an important tool for triaging and follow-up of patients with AD. More studies are needed to determine which teledermatology models are most effective for virtual assessment of AD.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".