Objective Sleep in Atopic Dermatitis: A Meta-Analysis
Bibliographic record
Abstract
Background: The evidence regarding objective sleep especially for the sleep architecture in atopic dermatitis (AD) was limited and not well summarized. Objective: To determine the objective sleep in AD patients as well as its confounders. Methods: We searched PubMed/Medline, Embase, and PsycInfo up to May 2021. Case–control studies or cohort studies that recruited AD patients and healthy controls and reported objective sleep parameters assessed by polysomnography or actigraphy were included. Results: A total of 7 studies with 173 AD patients and 122 controls were analyzed. Specifically, AD patients have significantly decreased total sleep time (TST, −13.797 minutes) and sleep efficiency (SE, −5.589%) accompanied by prolonged wake time after sleep onset (WASO, 29.972 minutes) and rapid eye movement sleep latency (31.894 minutes, all P < 0.05). Furthermore, subgroup analyses showed more WASO in severe AD subgroup compared with nonsevere AD subgroup (51.323 minutes vs 20.966 minutes, P = 0.032), less SE in male-majority subgroup compared with female-majority subgroup (−9.443% vs −4.997%, P = 0.018), and less TST in adult subgroup compared with child subgroup (−41.045 vs −4.016 minutes, P = 0.037). Conclusion: Objective sleep was worse in AD patients, especially among patients with severe AD, males, and adults. AD appears to more predispose difficulty in sleep maintenance rather than falling asleep.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.033 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".