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Record W3123672404 · doi:10.1111/iej.13477

PRIASE 2021 guidelines for reporting animal studies in Endodontology: a consensus‐based development

2021· review· en· W3123672404 on OpenAlexaff
Venkateshbabu Nagendrababu, Anil Kishen, Peter E. Murray, M. H. Nekoofar, José Antônio Poli de Figueiredo, E. Priya, Jayakumar Jayaraman, Shaju Jacob Pulikkotil, Josette Camilleri, Renato Silva, P. M. H. Dummer

Bibliographic record

VenueInternational Endodontic Journal · 2021
Typereview
Languageen
FieldVeterinary
TopicAnimal testing and alternatives
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsChecklistDelphi methodMedical educationDelphiPsychologyComputer-assisted web interviewingSteering committeeFlowchartEthics committeeMedicineMedical physicsComputer scienceEngineeringPolitical scienceEngineering managementBusiness

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)0.370
metaresearch head score (Gemma)0.464
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.630
Threshold uncertainty score0.777

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3700.464
Meta-epidemiology (narrow)0.0020.005
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0130.008
Science and technology studies0.0040.006
Scholarly communication0.0140.008
Open science0.0100.011
Research integrity0.0120.015
Insufficient payload (model declined to judge)0.0270.032

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 head0.821
GPT teacher head0.624
Teacher spread0.197 · 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
DomainReporting
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

Citations196
Published2021
Admission routes1
Has abstractyes

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