Are baths desirable in atopic dermatitis?
Bibliographic record
Abstract
BACKGROUND: Bathing is a therapeutic measure commonly advised in atopic dermatitis (AD). Whether baths improve skin condition remains yet unclear. Our objectives were to appreciate the effect of 1-month tap water bathing on the submerged skin of AD patients, and to evaluate the proportion of AD patients in favour of baths. METHODS: A Medline search of the last 30 years was performed in November 2012 using the Medical Subject Heading terms 'Atopic Dermatitis' OR 'Eczema' combined with 'Baths' OR 'Bath' OR 'Hydrotherapy' OR 'Cleansing' OR 'Soak'. Interventional studies measuring skin changes after tap water bathing were selected for the first analysis. Observational studies reporting the proportion of AD patients favourable to baths were selected for the second analysis. Important details regarding methods and results were independently extracted by two authors. Random-effects models were used to combine data on outcomes and conduct meta-analyses. RESULTS: Of the 271 abstracts initially identified, seven studies met the selection criteria. All were of good quality. The number of patients with AD ranged from 89 to 260 (41.4-66.3% female). The pooled effect size of skin changes after baths was -0.10 (95% confidence interval: -0.47 to 0.28) in random-effects meta-analysis. The meta-analytical pooled proportion of AD patients in favour of baths was 29.1% (18.5-42.7). CONCLUSIONS: We found no evidence of an effect of 1-month tap water bathing on skin changes in AD. Only 29 percent of AD patients were favourable to baths. Use of baths in AD should be decided together with the patient.
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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 | 0.020 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".