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Record W3123893780 · doi:10.1136/bmj.n160

PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews

2021· article· en· W3123893780 on OpenAlexaff
Matthew J. Page, David Moher, Patrick M. Bossuyt, Isabelle Boutron, Tammy Hoffmann, Cynthia D. Mulrow, Larissa Shamseer, Jennifer Tetzlaff, Elie A. Akl, Sue Brennan, Roger Chou, Julie Glanville, Jeremy Grimshaw, Asbjørn Hróbjartsson, Manoj M. Lalu, Tianjing Li, Elizabeth Loder, Evan Mayo‐Wilson, Steve McDonald, Luke A. McGuinness, Lesley Stewart, James Thomas, Andrea C. Tricco, Vivian Welch, Penny Whiting, Joanne E. McKenzie

Bibliographic record

VenueBMJ · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsBruyèrePublic Health OntarioUniversity of TorontoSt. Michael's HospitalQueen's UniversityMcMaster UniversityImpactOttawa HospitalUniversity of Ottawa
FundersNational Eye Institute
KeywordsSystematic reviewTerminologyGuidelineComputer scienceMEDLINEData scienceMedicineManagement sciencePathologyPolitical science

Abstract

fetched live from OpenAlex

The PRISMA 2020 statement includes a checklist of 27 items to guide reporting of systematic reviews In this article we explain why reporting of each item is recommended, present bullet points that detail the reporting recommendations, and present examples from published reviews We hope that uptake of the PRISMA 2020 statement will lead to more transparent, complete, and accurate reporting of systematic reviews, thus facilitating evidence based decision making on 1 September

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 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)0.341
metaresearch head score (Gemma)0.608
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.659
Threshold uncertainty score0.812

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3410.608
Meta-epidemiology (narrow)0.0080.016
Meta-epidemiology (broad)0.0130.033
Bibliometrics0.0220.031
Science and technology studies0.0030.007
Scholarly communication0.0130.011
Open science0.0120.012
Research integrity0.0140.033
Insufficient payload (model declined to judge)0.0930.062

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 head0.708
GPT teacher head0.551
Teacher spread0.157 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainReporting
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

Citations10,966
Published2021
Admission routes1
Has abstractyes

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