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Record W4415279421 · doi:10.1136/bmjebm-2024-113391

Over 1000 terms have been used to describe evidence synthesis: a scoping review

2025· review· en· W4415279421 on OpenAlexaff
Danielle Pollock, Sabira Hasanoff, Timothy Hugh Barker, Barbara Clyne, Andrea C. Tricco, Andrew Booth, Christina Godfrey, Hanan Khalil, Romy Menghao Jia, Petek Eylül Taneri, KM Saif‐Ur‐Rahman, M. Konstantinidis, Catherine Stratton, Deborah Edwards, Lyndsay Alexander, Judith Carrier, Nahal Habibi, Marco Zaccagnini, Cindy Stern, Chelsea Valenzuela, Carrie Price, Jennifer Stone, Edoardo Aromataris, Zoe Jordan, Mafalda M. Dias, Grace McKenzie McBride, Raju Kanukula, Holger J. Schünemann, Reem A. Mustafa, Miloslav Klugar, María Ximena Rojas, Pablo Alonso‐Coello, Paul Whaley, Miranda Langendam, Tracy Merlin, Sharon E. Straus, Sandeep Moola, Brian S. Alper, Zachary Munn

Bibliographic record

VenueBMJ evidence-based medicine · 2025
Typereview
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsUniversity of TorontoPublic Health OntarioImpactMcGill UniversityMcMaster UniversityQueen's UniversitySt. Michael's Hospital
FundersNational Health and Medical Research Council
KeywordsIdentification (biology)Scope (computer science)Context (archaeology)Key (lock)Perspective (graphical)Systematic review

Abstract

fetched live from OpenAlex

OBJECTIVE: To inform the development of an evidence synthesis taxonomy, we aimed to identify and examine all classification systems, typologies or taxonomies that have been proposed for evidence synthesis methods. DESIGN: Scoping review. METHODS: This review followed JBI (previously Joanna Briggs Institute) scoping review methodology and was reported according to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). Resources that investigated typologies, taxonomies, classification systems and compendia for evidence synthesis within any field were eligible for inclusion. A comprehensive search across MEDLINE (Ovid), Embase (OVID), CINAHL with Full-Text (EBSCO), ERIC (EBSCO), Scopus, Compendex (Elsevier) and JSTOR was performed on 28 April 2022. This was supplemented by citation searching of key articles, contact with experts, targeted searching of organisational websites and additional grey literature searching. Documents were extracted by one reviewer and extractions verified by another reviewer. Data were analysed using frequency counts and a basic qualitative content analysis approach. Results are presented using bar charts, word clouds and narrative summary. RESULTS: There were 15 634 titles and abstracts screened, and 703 full texts assessed for eligibility. Ultimately, 446 documents were included, and 49 formal classification systems identified, with the remaining documents presenting structured lists, simple listings or general discussions. Included documents were mostly not field-specific (n=242) or aligned to clinical sciences (n=83); however, public health, education, information technology, law and engineering were also represented. Documents (n=148) mostly included two to three evidence synthesis types, while 22 documents mentioned over 20 types of evidence synthesis. We identified 1010 unique terms to describe a type of evidence synthesis; of these, 742 terms were only mentioned once. Facets that could usefully distinguish (ie, similarities and differences or characteristics) between evidence synthesis approaches were categorised based on similarity into 15 overarching dimensions. These dimensions include review question and foci of interest, discipline/field, perspective, coverage, eligibility criteria, review purpose, methodological principles, theoretical underpinnings/philosophical perspective, resource considerations, compatibility with heterogeneity, sequence planning, analytical synthesis techniques, intended product/output, intended audience and intended impact or influence. CONCLUSION: This scoping review identified numerous unique terms to describe evidence synthesis approaches and many diverse ways to distinguish or categorise review types. These results suggest a need for the evidence synthesis community to organise, categorise and harmonise evidence synthesis approaches and terminolog.

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
gemmaMetaresearchBibliometrics
Domain: Reporting · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewlow
gptMetaresearch
Domain: Methods · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewmedium
models splitAgreement compares identical category sets and study designs across arms.

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)0.301
metaresearch head score (Gemma)0.669
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad), Open science, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.529
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.3010.669
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0410.009
Bibliometrics0.0030.009
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0100.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0350.007

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 head0.878
GPT teacher head0.637
Teacher spread0.241 · 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.

MetaresearchBibliometrics

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designSystematic review
DomainReporting · Methods
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

Citations2
Published2025
Admission routes1
Has abstractyes

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