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Record W2995826184 · doi:10.1136/bmj.k3750

DELTA<sup>2</sup> guidance on choosing the target difference and undertaking and reporting the sample size calculation for a randomised controlled trial

2018· article· en· W2995826184 on OpenAlexaff
Jonathan Cook, Steven A. Julious, William Sones, Lisa V. Hampson, Catherine Hewitt, Jesse A. Berlin, Deborah Ashby, Richard Emsley, Dean Fergusson, Stephen J. Walters, Ed Wilson, Graeme MacLennan, Nigel Stallard, Joanne Rothwell, Martin Bland, Louise Brown, Craig Ramsay, Andrew Cook, David Armstrong, Doug Altman, Luke Vale

Bibliographic record

VenueBMJ · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsOttawa Hospital
FundersMedical Research CouncilNational Institute for Health and Care Research
KeywordsSample size determinationSample (material)Type I and type II errorsStatistical powerKey (lock)Clinical trialComputer scienceRandomized controlled trialStatisticsMedical physicsPsychologyMedicineMathematicsComputer securityPhysics

Abstract

fetched live from OpenAlex

Central to the design of a randomised controlled trial is an a priori sample size calculation, which ensures a high probability of the study achieving its prespecified main objective An incorrect sample size can result in a study that is unable to inform clinical practice (hence directly or indirectly harming patients), or could expose excess patients to the uncertainty inherent in a clinical trial

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)0.029
metaresearch head score (Gemma)0.100
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.551
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0290.100
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.331
GPT teacher head0.445
Teacher spread0.115 · 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; both teacher heads agree on what is shown here.

Study designRandomized trial
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

Citations141
Published2018
Admission routes1
Has abstractyes

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