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Record W4404021399 · doi:10.1093/ibd/izae256

The Clinical Interpretation of Cluster Trials

2024· review· en· W4404021399 on OpenAlexaff
Pavel S Roshanov, Guangyong Zou, Reena Khanna

Bibliographic record

VenueInflammatory Bowel Diseases · 2024
Typereview
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsRobarts Clinical TrialsPopulation Health Research InstituteLawson Health Research InstituteWestern University
Fundersnot available
KeywordsInterpretation (philosophy)Cluster (spacecraft)Cluster randomised controlled trialPsychological interventionClinical trialComputer scienceMedical physicsMedicineData miningPathologyNursing

Abstract

fetched live from OpenAlex

Cluster-randomized trials randomize entire groups of participants, instead of individual participants, to different treatment arms. For certain interventions (eg, institutional policies, processes of care, treatment algorithms), these designs protect against contamination between study arms. However, cluster trials are logistically complex to implement and have unique vulnerabilities that must be evaluated for accurate interpretation. Here, we provide a general overview of the design and statistical issues in cluster trials to facilitate their interpretation by clinicians.

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)0.021
metaresearch head score (Gemma)0.450
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.894
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.450
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.573
GPT teacher head0.622
Teacher spread0.049 · 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 designOther design
Domainnot available
GenreReview

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

Citations0
Published2024
Admission routes1
Has abstractyes

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