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Diagnostic test accuracy network meta-analysis methods: A scoping review and empirical assessment

2022· review· en· W4213133915 on OpenAlexaff
Areti Angeliki Veroniki, Sofia Tsokani, Ridhi Agarwal, Eirini Pagkalidou, Gerta Rücker, Dimitris Mavridis, Yemisi Takwoingi

Bibliographic record

VenueJournal of Clinical Epidemiology · 2022
Typereview
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersNational Institute for Health and Care Research
KeywordsMeta-analysisMeta-regressionRegression analysisStatisticsComputer scienceLinear regressionData miningRegressionMedicineMathematicsPathology

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)0.266
metaresearch head score (Gemma)0.607
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.734
Threshold uncertainty score0.906

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2660.607
Meta-epidemiology (narrow)0.0050.003
Meta-epidemiology (broad)0.0200.029
Bibliometrics0.0220.015
Science and technology studies0.0010.003
Scholarly communication0.0100.010
Open science0.0060.006
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0060.001

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 head0.985
GPT teacher head0.812
Teacher spread0.173 · 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 designSystematic 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

Citations29
Published2022
Admission routes1
Has abstractno

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