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Record W2413385969 · doi:10.1002/jrsm.1215

Implications of applying methodological shortcuts to expedite systematic reviews: three case studies using systematic reviews from agri‐food public health

2016· article· en· W2413385969 on OpenAlexafffund
Mai Pham, Lisa Waddell, Andrijana Rajić, Jan M. Sargeant, Andrew Papadopoulos, Scott A. McEwen

Bibliographic record

VenueResearch Synthesis Methods · 2016
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsUniversity of GuelphPublic Health Agency of Canada
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsRigourSystematic reviewMeta-analysisPublication biasSystematic errorPublic healthComputer scienceMEDLINEManagement scienceStatisticsEconometricsPsychologyData scienceMedicineBiologyMathematicsEconomics

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.779
metaresearch head score (Gemma)0.914
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.221
Threshold uncertainty score0.273

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.7790.914
Meta-epidemiology (narrow)0.0040.005
Meta-epidemiology (broad)0.0070.018
Bibliometrics0.0180.023
Science and technology studies0.0060.010
Scholarly communication0.0170.017
Open science0.0100.017
Research integrity0.0180.016
Insufficient payload (model declined to judge)0.0040.001

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 head0.992
GPT teacher head0.766
Teacher spread0.226 · 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 designQualitative
DomainMethods
GenreEmpirical

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

Citations43
Published2016
Admission routes2
Has abstractyes

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