Steroid Withdrawal Effects Following Long-term Topical Corticosteroid Use
Bibliographic record
Abstract
BACKGROUND: Concerns about topical steroid withdrawal (TSW) are leading some patients to cease long-term topical corticosteroid (TCS) therapy. Diagnostic criteria for this condition do not exist. OBJECTIVE: The aim of this study was to examine the demographics and outcomes in adult patients who believe they are experiencing TSW following discontinuation of chronic TCS overuse. METHODS: This was a retrospective cohort study of patients in an Australian general practice presenting with this clinical scenario between January 2015 and February 2018. RESULTS: Women represented 56% of the 55 patients seen, and ages ranged from 20 to 66 years (mean, 32.9 years; median, 30.0 years). Seventy-six percent had an original diagnosis of atopic dermatitis. Sixty percent had used potent TCSs on the face, and 42% had a history of oral corticosteroid use for skin symptoms. Burning pain was reported in 65%; all had widespread areas of red skin; and so-called "elephant wrinkles," "red sleeve," and the headlight sign were seen in 56%, 40%, and 29%, respectively. CONCLUSIONS: Patients with a history of long-term TCS overuse may experience symptoms and signs described in TSW on stopping TCSs. Diagnostic criteria, reflecting the histories and examination findings of the patients studied, are suggested in this article with the aim to advance discussion and research into 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".