Parent satisfaction with lotion, cream, gel and ointment emollient types: secondary analysis of the Best Emollients for Eczema study
Bibliographic record
Abstract
BACKGROUND: The main determinant of emollient effectiveness is whether it is used, which in turn is linked with user satisfaction. OBJECTIVES: To compare parental satisfaction with emollient type for the treatment of childhood eczema. METHODS: Secondary analysis of data from the Best Emollients for Eczema (BEE) trial was undertaken. In total, 550 children aged between 6 months and 12 years were recruited from primary care in England and randomized to use a lotion, cream, gel or ointment as their main emollient for 16 weeks. At week, 16 parents were asked to complete an Emollient Satisfaction Questionnaire (ESQ). Completion rates and scores were compared, using χ2 test, t-test calculations and one-way Anova as appropriate. RESULTS: Data on 378 participants (68.7% of those randomized) were analysed. Mean ESQ scores were gel 20.9 (SD 5.3), lotion 20.4 (SD 5.6), cream 18.8 (SD 6.3) and ointment 15.2 (SD 6.8) (P < 0.001). In pairwise comparisons, there was a statistically significant difference in mean ESQ scores between ointment and lotion (P < 0.001), ointment and cream (P < 0.001) and ointment and gel (P < 0.001) but not between lotion, cream and gel. Participants using lotions had highest overall satisfaction and were most likely to continue using their emollient. ESQ scores were correlated with reported emollient use and improvements in parent-reported eczema severity. CONCLUSIONS: Overall, lotions and gels were favoured over creams and ointments. Although satisfaction is determined by personal preference, these results will aid parents, clinicians and children to find the right emollient(s) for them.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".