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Record W7075582508

Reporting of multi-arm parallel-group randomized trials: Extension of the consort 2010 statement

2019· article· en· W7075582508 on OpenAlexfundno aff

Bibliographic record

VenueOxford University Research Archive (ORA) (University of Oxford) · 2019
Typearticle
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsnot available
FundersSchool of Medicine, University of North Carolina at Chapel HillUniversity of Texas Health Science Center at San AntonioFeinberg School of MedicineUniversity of North Carolina at Chapel HillUniversity of TorontoMargaret A. Cargill FoundationUniversity of LeedsFHI 360McMaster UniversityOttawa Hospital Research InstituteUniversity of OxfordNorthwestern University
KeywordsConsolidated Standards of Reporting TrialsChecklistGuidelineRandomized controlled trialTransparency (behavior)Clinical trialQuality (philosophy)Research design
DOInot available

Abstract

fetched live from OpenAlex

<p style="text-align:justify;"> <b>Importance:</b> The quality of reporting of randomized clinical trials is suboptimal. In an era in which the need for greater research transparency is paramount, inadequate reporting hinders assessment of the reliability and validity of trial findings. The Consolidated Standards of Reporting Trials (CONSORT) 2010 Statement was developed to improve the reporting of randomized clinical trials, but the primary focus was on parallel-group trials with 2 groups. Multi-arm trials that use a parallel-group design (comparing treatments by concurrently randomizing participants to one of the treatment groups, usually with equal probability) but have 3 or more groups are relatively common. The quality of reporting of multi-arm trials varies substantially, making judgments and interpretation difficult. While the majority of the elements of the CONSORT 2010 Statement apply equally to multi-arm trials, some elements need adaptation, and, in some cases, additional issues need to be clarified.<br/><br/> <b>Objective:</b> To present an extension to the CONSORT 2010 Statement for reporting multi-arm trials to facilitate the reporting of such trials.<br/><br/> <b>Design:</b> A guideline writing group, which included all authors, formed following the CONSORT group meeting in 2014. The authors met in person and by teleconference bimonthly between 2014 and 2018 to develop and revise the checklist and the accompanying text, with additional discussions by email. A draft manuscript was circulated to the wider CONSORT group of 36 individuals, plus 5 other selected individuals known for their specialist knowledge in clinical trials, for review. Extensive feedback was received from 14 individuals and, after detailed consideration of their comments, a final revised version of the extension was prepared.<br/><br/> <b>Findings:</b> This CONSORT extension for multi-arm trials expands on 10 items of the CONSORT 2010 checklist and provides examples of good reporting and a rationale for the importance of each extension item. Key recommendations are that multi-arm trials should be identified as such and require clear objectives and hypotheses referring to all of the treatment groups. Primary treatment comparisons should be identified and authors should report the planned and unplanned comparisons resulting from multiple groups completely and transparently. If statistical adjustments for multiplicity are applied, the rationale and method used should be described.<br/><br/> <b>Conclusions and Relevance:</b> This extension of the CONSORT 2010 Statement provides specific guidance for the reporting of multi-arm parallel-group randomized clinical trials and should help provide greater transparency and accuracy in the reporting of such trials. </p>

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)0.009
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.832

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.093
GPT teacher head0.331
Teacher spread0.238 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2019
Admission routes1
Has abstractyes

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