Do Patient Characteristics Matter When Calculating Sample Size for Eczema Clinical Trials?
Bibliographic record
Abstract
Background: The Patient-Oriented Eczema Measure (POEM) is the core outcome instrument recommended for measuring patient-reported atopic eczema symptoms in clinical trials. To ensure that the statistical significance of clinical trial results is meaningful, trials are often designed by specifying the target difference in the primary outcome as part of the sample size calculation. One method used to specify the target difference is a score that corresponds to a standardized effect size. Objectives: to assess how the standardized effect size of POEM scores vary across age, gender, ethnicity and disease severity. Methods: This study combined data from five UK-based randomized clinical trials of eczema treatments in order to assess differences in self-reported eczema symptoms (POEM) corresponding to a standardized effect size (0.5 SD of baseline POEM scores) across age, gender, ethnicity and disease severity. Results: were remarkably consistent across participants of varying ages, gender, ethnicity and disease severity from datasets of five UK trials in children (range 2.99-3.45). Conclusions: This study provides information that can support those designing clinical trials to determine their sample size and can aid individuals interpreting trial results. Further exploration of differences in populations beyond the United Kingdom is needed.
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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 | 0.546 | 0.782 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".