A detailed evaluation of the effect of dupilumab on sleep in adults with atopic dermatitis
Bibliographic record
Abstract
While itch is considered the main symptom and an important treatment target in atopic dermatitis (AD), the burden of the disease is multifactorial.1 One central driver of AD burden is sleep disturbance, with consequent daytime sleepiness and fatigue.2 Dupilumab has been shown to improve sleep in adults with AD in five clinical trials that used either sleepiness visual analogue scale (VAS) from SCORing Atopic Dermatitis (SCORAD) or frequency of sleep disruption owing to AD from the Patient-Oriented Eczema Measure.3 However, those trials did not study the effect of dupilumab on sleep quality and duration in detail. In this issue of the BJD, Merola et al. report results from Dupilumab Effect on Sleep in AD Patients (DUPISTAD), a randomized placebo-controlled trial examining the effect of dupilumab on sleep after 12 weeks of treatment in 127 patients aged ≥ 18 years with moderate-to-severe AD.4 The study used multiple metrics to study sleep, including a modified sleep numeric rating scale (NRS) (the primary outcome), sleep-related impairment (SRI) T-score and the Epworth Sleepiness Scale (ESS). Sleep NRS significantly improved among patients treated with dupilumab compared with placebo at week 12, as did SCORAD sleep VAS, SRI T-score and ESS score. The improved sleep outcomes were seen as early as week 2 of treatment. Similarly to other trials, improvements were also seen in disease severity and quality of life (QoL). The study also used sleep diary and actigraphy data to assess sleep efficiency, total sleep time, wake after sleep onset, and sleep onset latency – there was no difference in these outcomes between the groups.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".