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Record W3161034762 · doi:10.1002/ski2.42

Do Patient Characteristics Matter When Calculating Sample Size for Eczema Clinical Trials?

2021· article· en· W3161034762 on OpenAlexaff
Laura Howells, Sonia Gran, Joanne R Chalmers, Beth Stuart, Miriam Santer, Lucy Bradshaw, Daisy Gaunt, Matthew J Ridd, Louise A. A. Gerbens, Phyllis I. Spuls, Chenchen Huang, Nick Francis, Kim S Thomas

Bibliographic record

VenueSkin Health and Disease · 2021
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsInstitute of Infection and Immunity
FundersResearch for Patient Benefit ProgrammeProgramme Grants for Applied ResearchHealth Technology Assessment ProgrammeBritish Skin FoundationNational Institute for Health and Care Research
KeywordsClinical trialSample size determinationEthnic groupMedicineRandomized controlled trialPhysical therapyInternal medicineStatistics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.026
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.097
GPT teacher head0.436
Teacher spread0.338 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2021
Admission routes1
Has abstractyes

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