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Record W3089055170 · doi:10.1037/hea0001020

Balance of group sizes in randomized controlled trials published in American Psychological Association journals.

2020· article· en· W3089055170 on OpenAlexaff
Mara Cañedo-Ayala, Danielle B. Rice, A.H. Levis, Matthew J. Chiovitti, Brett D. Thombs

Bibliographic record

VenueHealth Psychology · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsRandomizationPsycINFORandomized controlled trialSample size determinationOdds ratioMedicineConfidence intervalLogistic regressionPsychologyMEDLINEStatisticsInternal medicineMathematics

Abstract

fetched live from OpenAlex

OBJECTIVE: We evaluated whether sample size differences between arms of two-arm parallel group randomized controlled trials (RCTs) published in American Psychological Association (APA)-affiliated journals were consistently smaller than expected by chance with simple randomization. METHOD: We searched PsycINFO for two-arm parallel group RCTs in APA-affiliated journals published January 2007 to September 2017 that used individual randomization (1:1 allocation ratio), reported the number of participants randomized, and did not describe employing restrictive randomization (e.g., blocking). We queried authors because randomization processes were often not described in articles, and we conducted a post hoc logistic regression analysis to attempt to identify factors associated with overly balanced groups. RESULTS: ≤ 100); greater balance may be more common in higher impact journals, though this was not statistically significant. CONCLUSIONS: Education is needed to ensure that randomization procedures are implemented as intended and fully and accurately reported and that balanced group sample sizes are not understood as an indicator of trial quality. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

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)0.588
metaresearch head score (Gemma)0.559
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (broad), Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.5880.559
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0330.003
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.765
GPT teacher head0.644
Teacher spread0.121 · 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 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

Citations8
Published2020
Admission routes1
Has abstractyes

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