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Record W2952992205 · doi:10.1371/journal.pmed.1002819

Government policy interventions to reduce human antimicrobial use: A systematic review and evidence map

2019· review· en· W2952992205 on OpenAlexafffund
Susan Rogers Van Katwyk, Jeremy Grimshaw, Miriam Nkangu, Ranjana Nagi, Marc Mendelson, Monica Taljaard, Steven J. Hoffman

Bibliographic record

VenuePLoS Medicine · 2019
Typereview
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsImpactOttawa HospitalMcMaster UniversityYork UniversityUniversity of Ottawa
FundersCanadian Institutes of Health ResearchNorges ForskningsrådGovernment of OntarioOntario Ministry of Research, Innovation and ScienceUniversity of OttawaPublic Health AgencyPublic Health Agency of Canada
KeywordsCINAHLPsychological interventionMedicineHealth policyGovernment (linguistics)Systematic reviewRandomized controlled trialReimbursementMEDLINEPublic healthPolitical scienceHealth careNursingSurgery

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)0.036
metaresearch head score (Gemma)0.134
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.036
Threshold uncertainty score0.191

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.134
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0100.009
Bibliometrics0.0230.021
Science and technology studies0.0010.002
Scholarly communication0.0050.005
Open science0.0030.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.125
GPT teacher head0.390
Teacher spread0.265 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
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

Citations134
Published2019
Admission routes2
Has abstractyes

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