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Record W3163788934 · doi:10.1186/s12874-021-01295-w

Quality assessment tools used in systematic reviews of in vitro studies: A systematic review

2021· review· en· W3163788934 on OpenAlexfundno aff
Linh Tran, Dao Ngoc Hien Tam, Abdelrahman Elshafay, Thao Dang, Kenji Hirayama, Nguyen Tien Huy

Bibliographic record

VenueBMC Medical Research Methodology · 2021
Typereview
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsnot available
FundersAl-Azhar UniversityZagazig UniversityMcMaster University
KeywordsSystematic reviewMedical physicsProtocol (science)GuidelineScopusQuality assessmentMedicineComputer scienceMEDLINEPathologyExternal quality assessmentAlternative medicineBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Systematic reviews (SRs) and meta-analyses (MAs) are commonly conducted to evaluate and summarize medical literature. This is especially useful in assessing in vitro studies for consistency. Our study aims to systematically review all available quality assessment (QA) tools employed on in vitro SRs/MAs. METHOD: A search on four databases, including PubMed, Scopus, Virtual Health Library and Web of Science, was conducted from 2006 to 2020. The available SRs/MAs of in vitro studies were evaluated. DARE tool was applied to assess the risk of bias of included articles. Our protocol was developed and uploaded to ResearchGate in June 2016. RESULTS: Our findings reported an increasing trend in publication of in vitro SRs/MAs from 2007 to 2020. Among the 244 included SRs/MAs, 126 articles (51.6%) had conducted the QA procedure. Overall, 51 QA tools were identified; 26 of them (51%) were developed by the authors specifically, whereas 25 (49%) were pre-constructed tools. SRs/MAs in dentistry frequently had their own QA tool developed by the authors, while SRs/MAs in other topics applied various QA tools. Many pre-structured tools in these in vitro SRs/MAs were modified from QA tools of in vivo or clinical trials, therefore, they had various criteria. CONCLUSION: Many different QA tools currently exist in the literature; however, none cover all critical aspects of in vitro SRs/MAs. There is a need for a comprehensive guideline to ensure the quality of SR/MA due to their precise nature.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearchMeta-epidemiology (broad)
Domain: Methods · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
gptMetaresearchMeta-epidemiology (broad)
Domain: Methods · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
models agreeAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.413
metaresearch head score (Gemma)0.684
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.587
Threshold uncertainty score0.724

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4130.684
Meta-epidemiology (narrow)0.0050.004
Meta-epidemiology (broad)0.0200.025
Bibliometrics0.0510.039
Science and technology studies0.0040.006
Scholarly communication0.0120.011
Open science0.0060.008
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.995
GPT teacher head0.820
Teacher spread0.175 · 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

Labeled directly by 2 models reading the full record.

Study designSystematic review
DomainMethods
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

Citations126
Published2021
Admission routes1
Has abstractyes

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