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Record W4392712333 · doi:10.1136/bmjopen-2024-084164

Protocol for the development of a tool (INSPECT-SR) to identify problematic randomised controlled trials in systematic reviews of health interventions

2024· article· en· W4392712333 on OpenAlexaff
Jack Wilkinson, Calvin Heal, George Α. Antoniou, Ella Flemyng, Žarko Alfirević, Alison Avenell, Nicholas J. L. Brown, J. B. Carlisle, Mike Clarke, Patrick Dicker, Jo C Dumville, Andrew Grey, Steph Grohmann, Lyle C. Gurrin, Jill A. Hayden, James Heathers, Kylie E Hunter, Toby J Lasserson, Emily Lam, Sarah Lensen, Tianjing Li, Wentao Li, Elizabeth Loder, Andreas Lundh, Gideon Meyerowitz‐Katz, Ben W. Mol, Neil E O’Connell, Lisa Parker, Barbara K. Redman, Anna Lene Seidler, Kyle Sheldrick, Emma Sydenham, David Torgerson, Madelon van Wely, Rui Wang, Lisa Bero, Jamie J Kirkham

Bibliographic record

VenueBMJ Open · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsDalhousie University
FundersNational Eye InstituteResearch for Patient Benefit ProgrammeDepartment of Health and Social CareNational Institute for Health and Care Research
KeywordsSystematic reviewMedicineProtocol (science)Psychological interventionDelphi methodRandomized controlled trialDelphiMEDLINEHealth careEvidence-based medicineData extractionData scienceAlternative medicineManagement scienceComputer scienceNursingArtificial intelligenceSurgeryPathology

Abstract

fetched live from OpenAlex

INTRODUCTION: Randomised controlled trials (RCTs) inform healthcare decisions. It is now apparent that some published RCTs contain false data and some appear to have been entirely fabricated. Systematic reviews are performed to identify and synthesise all RCTs that have been conducted on a given topic. While it is usual to assess methodological features of the RCTs in the process of undertaking a systematic review, it is not usual to consider whether the RCTs contain false data. Studies containing false data therefore go unnoticed and contribute to systematic review conclusions. The INveStigating ProblEmatic Clinical Trials in Systematic Reviews (INSPECT-SR) project will develop a tool to assess the trustworthiness of RCTs in systematic reviews of healthcare-related interventions. METHODS AND ANALYSIS: The INSPECT-SR tool will be developed using expert consensus in combination with empirical evidence, over five stages: (1) a survey of experts to assemble a comprehensive list of checks for detecting problematic RCTs, (2) an evaluation of the feasibility and impact of applying the checks to systematic reviews, (3) a Delphi survey to determine which of the checks are supported by expert consensus, culminating in, (4) a consensus meeting to select checks to be included in a draft tool and to determine its format and (5) prospective testing of the draft tool in the production of new health systematic reviews, to allow refinement based on user feedback. We anticipate that the INSPECT-SR tool will help researchers to identify problematic studies and will help patients by protecting them from the influence of false data on their healthcare. ETHICS AND DISSEMINATION: The University of Manchester ethics decision tool was used, and this returned the result that ethical approval was not required for this project (30 September 2022), which incorporates secondary research and surveys of professionals about subjects relating to their expertise. Informed consent will be obtained from all survey participants. All results will be published as open-access articles. The final tool will be made freely available.

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)0.808
metaresearch head score (Gemma)0.383
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (broad), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.425
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.8080.383
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0230.006
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0030.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.926
GPT teacher head0.713
Teacher spread0.214 · 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 designSystematic review
Domainnot available
GenreProtocol

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

Citations37
Published2024
Admission routes1
Has abstractyes

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