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Same family, different species: methodological conduct and quality varies according to purpose for five types of knowledge synthesis

2017· review· en· W2769378808 on OpenAlexafffund
Andrea C. Tricco, Wasifa Zarin, Marco Ghassemi, Vera Nincic, Erin Lillie, Matthew J. Page, Larissa Shamseer, Jesmin Antony, Patricia Rios, Jeremiah Hwee, Areti Angeliki Veroniki, David Moher, Lisa Hartling, Ba’ Pham, Sharon E. Straus

Bibliographic record

VenueJournal of Clinical Epidemiology · 2017
Typereview
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsCanada Research ChairsOttawa HospitalUniversity of TorontoUniversity of AlbertaSt. Michael's Hospital
FundersNational Health and Medical Research CouncilMedical Research CouncilCanadian Institutes of Health ResearchUniversity of Ottawa
KeywordsSystematic reviewMedicineProtocol (science)CitationMissing dataMEDLINEMedical physicsStatisticsAlternative medicineComputer scienceMathematicsPathologyLibrary science

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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
gemmaMetaresearch
Domain: Methods · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptMetaresearchMeta-epidemiology (broad)
Domain: Methods · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
models splitAgreement 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)0.360
metaresearch head score (Gemma)0.773
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (broad)
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score0.789

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3600.773
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0100.017
Bibliometrics0.0260.031
Science and technology studies0.0040.011
Scholarly communication0.0170.016
Open science0.0040.010
Research integrity0.0100.010
Insufficient payload (model declined to judge)0.0070.002

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 head0.994
GPT teacher head0.793
Teacher spread0.201 · 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.

MetaresearchMeta-epidemiology (broad)

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

Study designObservational · Systematic 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
Published2017
Admission routes2
Has abstractno

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