Dermatologists’ Perceptions of the Use of Teledermatology in Managing Hidradenitis Suppurativa: Survey Study
Bibliographic record
Abstract
BACKGROUND: The field of teledermatology has expanded tremendously and has been used for conditions including hidradenitis suppurativa (HS). However, due to the sensitive location of lesions, HS may be considered less suitable for teledermatology. OBJECTIVE: We sought to assess dermatologists' experiences and perceptions toward using teledermatology for HS relative to atopic dermatitis (AD) as a comparison. METHODS: A survey was disseminated electronically to practicing dermatologists in the Asia-Pacific region between February and June 2022. Differences in attitudes and perceptions between HS and AD were compared using random-effects ordered logistic regression, controlling for demographics. RESULTS: A total of 100 responses were obtained comprising of 76 (81.7%) dermatologists and 17 (18.3%) dermatology trainees; 62.6% (62/98) of physicians were uncomfortable with using teledermatology for HS. Multivariable regression confirmed increased perceived challenges with managing HS using teledermatology compared to AD. These challenges include the need for photography of hard-to-reach or sensitive areas (odds ratio [OR] 4.71, 95% CI 2.44-9.07; P<.001), difficulties in accurate assessment of severity (OR 2.66, 95%CI 1.48-4.79; P=.001), and inability to palpate lesions (OR 2.27, 95% CI 1.23-4.18; P=.009). CONCLUSIONS: This study confirms the relative reluctance of dermatologists to use teledermatology for HS and complements existing data showing mixed levels of willingness from patients. The use of teledermatology for HS may need to be optimized to overcome these challenges, including increasing security features, selection of patients with milder or limited diseases, and selecting patients with an established and strong doctor-patient relationship.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".