Topical steroid withdrawal: self-diagnosis, unconscious bias and social media
Bibliographic record
Abstract
Abstract Background Consensus amongst dermatologists regarding the phenomenon of topical steroid withdrawal (TSW) is elusive. This may be contrasted with a growing online patient movement, including social media communities. Objectives This study aimed to investigate dermatologist perspectives regarding TSW and to assess attitudes towards self-diagnosis. Methods A two-part online questionnaire was disseminated to UK-based Dermatology Consultants, Registrars and Fellows. Section one presented a clinical scenario and randomized respondents into two groups: one mentioning TSW self-diagnosis, and an otherwise identical control without the self-diagnosis. Questions about the clinical scenario were directed to dermatologists and focused on attitudes regarding patient-predicted behaviours. Section two asked about TSW perceptions and experiences, and thematic analysis of open text responses was undertaken. Results One hundred and three responses were received, including 51 Consultants, 38 Trainee Dermatologists, 10 Dermatology Fellows, 3 Specialty And Specialist (SAS) Dermatology doctors and 1 Post-CCT (Certificate of Completion of Training) Fellow. Thirty-four percent (n = 35/103) of respondents considered TSW to be a distinct clinical entity, 17.5% (n = 18/103) did not and 48.5% (n = 50/103) were unsure. Respondents felt that self-diagnosing TSW patients were less likely to comply with treatment, and more likely to take up time and pose management problems compared with controls. Themes of uncertainty regarding diagnostic veracity and social media misinformation were identified. Conclusions Uncertainty regarding the veracity of a TSW diagnosis and its management is common amongst dermatology healthcare professionals (HCPs). Dermatology HCPs in this study considered that patients who self-diagnosed TSW were more difficult to engage with skin disease management. Dermatologists desire further understanding of and research into the nature and management of TSW.
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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 | 0.012 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".