Health Care Utilization in Patients With Atopic Dermatitis Experiencing Topical Steroid Withdrawal: Observational Cross-Sectional Social Media Questionnaire Study
Bibliographic record
Abstract
Background: Topical steroid withdrawal (TSW) is a controversial skin condition among health care providers due to a lack of evidence, but it has an impactful and growing presence on social media. There are few previous reports of health care utilization for symptoms attributed to TSW. Objective: This study aims to investigate health care utilization and requests as well as information sources for TSW among patients with atopic dermatitis (AD). Methods: This observational cross-sectional study used a questionnaire aimed at adults with AD, experiencing symptoms they attribute to TSW. The questionnaire was posted as a link, free to share with others, in a Swedish TSW-themed Facebook group and remained accessible for 4 weeks. Descriptive statistics and topical text analysis on open-ended items were used to present and interpret the results. Results: The participants (n=82) reported dermatologists (n=41, 50%), general practitioners (n=40, 49%), and practitioners of complementary and alternative medicine (CAM; n=32, 39%) as the most frequent health care contacts for TSW. However, among participants with ongoing symptoms attributed to TSW (n=68), ongoing health care contacts with general practitioners, dermatologists, and practitioners of CAM were reported by only 10% (n=7), 22% (n=15), and 13% (n=11), respectively. For symptoms attributed to AD, the frequencies of health care provider contacts were higher. Almost all participants had sought help from a general practitioner (n=81, 99%) or a dermatologist (n=76, 93%) at some point, and many had also consulted a practitioner of CAM (n=59, 72%). Among those with ongoing symptoms attributed to AD, 43% (n=26) had an ongoing contact with a dermatologist. Participant-requested help and support from health care providers included understanding and confirmation of TSW impairments (n=45, 56%), treatment of symptoms (n=26, 32%), and increased awareness and information about TSW from health care providers (n=21, 26%). The most common TSW information sources were Facebook (n=78, 96%), websites (n=75, 93%), and Instagram (n=45, 56%), but YouTube (n=11, 14%), podcasts (n=7, 10%), and TikTok (n=5, 6%) were also reported. Conclusions: This study investigates health care utilization patterns related to TSW. The results indicate that the participants received insufficient support from health care providers for symptoms they attributed to TSW. The participants initiated and maintained health care provider contacts for symptoms attributed to AD to a greater extent than for TSW and sought information and support for TSW elsewhere. Targeted interventions to overcome this could be educational efforts for general practitioners and dermatologists about the current scientific knowledge of TSW as well as the TSW discourse on social media. In addition, health care providers need to engage and contribute to evidence-based content about TSW on relevant social media platforms to prevent the spread of misinformation about topical glucocorticoids.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".