How to use the Harmonising Outcome Measures for Eczema Core Outcome Set for atopic dermatitis trials: a users’ guide
Bibliographic record
Abstract
BACKGROUND: The Harmonising Outcome Measures for Eczema (HOME) initiative has agreed upon the Core Outcome Set (COS) for use in atopic dermatitis (AD) clinical trials, but additional guidance is needed to maximize its uptake. OBJECTIVES: To provide answers to some of the commonly asked questions about using the HOME COS; to provide data to help with the interpretation of trial results; and to support sample size calculations for future trials. METHODS AND RESULTS: We provide practical guidance on the use of the HOME COS for investigators planning clinical trials in patients with AD. It answers some of the common questions about using the HOME COS, how to access the outcome measurement instruments, what training/resources are needed to use them appropriately and clarifies when the COS is applicable. We also provide exemplar data to inform sample size calculations for eczema trials and encourage standardized data collection and reporting of the COS. CONCLUSIONS: By encouraging adoption of the COS and facilitating consistent reporting of outcome data, it is hoped that the results of eczema trials will be more comprehensive and readily combined in meta-analyses and that patient care will subsequently be improved.
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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 | 0.150 | 0.368 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.093 | 0.057 |
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".