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Record W2906277950 · doi:10.1016/j.jval.2018.09.2215

PRM94 - ALIGNING TEXT MINING AND MACHINE LEARNING ALGORITHMS WITH BEST PRACTICES FOR STUDY SELECTION IN SYSTEMATIC LITERATURE REVIEWS

2018· article· en· W2906277950 on OpenAlexaff
Evan Popoff, JP Jansen, M. Besada, Shannon Cope, Steve Kanters

Bibliographic record

VenueValue in Health · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsPrecision Nanosystems (Canada)
Fundersnot available
KeywordsMachine learningArtificial intelligenceComputer scienceAlgorithmSystematic reviewSupport vector machineBootstrapping (finance)PopulationNaive Bayes classifierData miningMedicineMEDLINEMathematics

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)0.171
metaresearch head score (Gemma)0.502
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.829
Threshold uncertainty score0.907

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1710.502
Meta-epidemiology (narrow)0.0030.005
Meta-epidemiology (broad)0.0070.016
Bibliometrics0.0170.019
Science and technology studies0.0030.002
Scholarly communication0.0090.005
Open science0.0060.012
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0310.009

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 head0.723
GPT teacher head0.551
Teacher spread0.171 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
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

Citations0
Published2018
Admission routes1
Has abstractno

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