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Record W3022156859 · doi:10.1186/s12874-020-00999-9

An assessment of the quality of current clinical meta-analyses

2020· article· en· W3022156859 on OpenAlexaff
Irbaz Hameed, Michelle Demetres, Derrick Y. Tam, Mohamed Rahouma, Faiza Khan, Drew Wright, Keith Mages, Antonio P. DeRosa, Becky Baltich Nelson, Kevin Pain, Diana Delgado, Leonard N. Girardi, Stephen E. Fremes, Mario Gaudino

Bibliographic record

VenueBMC Medical Research Methodology · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsQuality (philosophy)Current (fluid)MEDLINEMedicinePsychologyData scienceComputer sciencePolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: The objective of this study was to assess the overall quality of study-level meta-analyses in high-ranking journals using commonly employed guidelines and standards for systematic reviews and meta-analyses. METHODS: 100 randomly selected study-level meta-analyses published in ten highest-ranking clinical journals in 2016-2017 were evaluated by medical librarians against 4 assessments using a scale of 0-100: the Peer Review of Electronic Search Strategies (PRESS), Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), Institute of Medicine's (IOM) Standards for Systematic Reviews, and quality items from the Cochrane Handbook. Multiple regression was performed to assess meta-analyses characteristics' associated with quality scores. RESULTS: The overall median (interquartile range) scores were: PRESS 62.5(45.8-75.0), PRISMA 92.6(88.9-96.3), IOM 81.3(76.6-85.9), and Cochrane 66.7(50.0-83.3). Involvement of librarians was associated with higher PRESS and IOM scores on multiple regression. Compliance with journal guidelines was associated with higher PRISMA and IOM scores. CONCLUSION: This study raises concerns regarding the reporting and methodological quality of published MAs in high impact journals Early involvement of information specialists, stipulation of detailed author guidelines, and strict adherence to them may improve quality of published meta-analyses.

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)0.865
metaresearch head score (Gemma)0.919
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Open science, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.710
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.8650.919
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0100.007
Bibliometrics0.0000.003
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0080.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0430.000

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 head0.999
GPT teacher head0.886
Teacher spread0.113 · 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
DomainMethods
GenreMethods

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

Citations25
Published2020
Admission routes1
Has abstractyes

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