Preventing eczema flares with topical corticosteroids or tacrolimus: which is best?
Bibliographic record
Abstract
Just over 10 years ago, I was involved in undertaking an overarching Health Technology Assessment systematic review of treatments for atopic eczema.1 Given that eczema2 lasts for many months or years in most people,3 it was disappointing to see that nearly all of the 272 randomized controlled trials included in that review were of short duration (6 weeks or less).1 Showing that a treatment can improve eczema in the short term is of course important, but studies that identify strategies to maintain control are equally, if not more important, in a chronic condition. The systematic review called for longer‐term studies in eczema, so it is heartening to see two publications in this month’s BJD reviewing a number of studies that aim to prevent flares in people with eczema who are initially controlled.4, 5 One of the reports evaluates the cost‐effectiveness of topical tacrolimus when used in traditional ‘reactive’ mode (i.e. applying topical tacrolimus ‘as needed’ during disease exacerbations) vs. ‘proactive’ regular weekly treatment (i.e. using the active topical agent for two consecutive days each week).4 The other report systematically reviews the evidence of several trials that have compared reactive vs. proactive therapy for both topical tacrolimus and topical corticosteroids.5
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.038 | 0.030 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".