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The web-based “Right Review” tool asks reviewers simple questions to suggest methods from 41 knowledge synthesis methods

2022· article· en· W4221135745 on OpenAlexafffund
Krystle Amog, Ba’ Pham, Melissa Courvoisier, Megan Mak, Andrew Booth, Christina Godfrey, Jeremiah Hwee, Sharon E. Straus, Andrea C. Tricco

Bibliographic record

VenueJournal of Clinical Epidemiology · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsQueen's UniversityPublic Health OntarioUniversity of TorontoSt. Michael's Hospital
FundersGovernment of Canada
KeywordsSimple (philosophy)Computer scienceWeb siteInformation retrievalData scienceWorld Wide WebMedicineThe InternetEpistemology

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

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)0.195
metaresearch head score (Gemma)0.589
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.805
Threshold uncertainty score0.993

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1950.589
Meta-epidemiology (narrow)0.0040.005
Meta-epidemiology (broad)0.0070.008
Bibliometrics0.0290.012
Science and technology studies0.0030.002
Scholarly communication0.0120.015
Open science0.0040.010
Research integrity0.0100.006
Insufficient payload (model declined to judge)0.2280.064

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 head0.876
GPT teacher head0.725
Teacher spread0.150 · 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
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

Citations49
Published2022
Admission routes2
Has abstractno

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