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Transmission Surveillance for Antimicrobial-Resistant Organisms in the Health System

2018· review· en· W2890466699 on OpenAlexaff
Johann Pitout

Bibliographic record

VenueMicrobiology Spectrum · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsCalgary Laboratory ServicesUniversity of Calgary
FundersNational Center for Emerging and Zoonotic Infectious DiseasesNational Center for HIV/AIDS, Viral Hepatitis, STD, and TB PreventionNational Center for Immunization and Respiratory DiseasesCenters for Disease Control and Prevention
KeywordsTransmission (telecommunications)Environmental healthDisease transmissionGeographyBiologyComputer scienceMedicineVirologyTelecommunications

Abstract

fetched live from OpenAlex

Surveillance of antibiotic resistance involves the collection of antibiotic susceptibility patterns undertaken by clinical microbiology laboratories on bacteria isolated from clinical specimens. Global surveillance programs have shown that antibiotic resistance is a major threat to the public at large and play a crucial role in the development of enhanced diagnostics as well as potential vaccines and novel antibiotics with activity against antimicrobial-resistant organisms. This review focuses primarily on examples of global surveillance systems. Local, national, and global integrated surveillance programs with sufficient data linkage between these schemes, accompanied by enhanced genomics and user-friendly bioinformatics systems, promise to overcome some of the stumbling blocks encountered in the understanding, emergence, and transmission of antimicrobial-resistant organisms.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.016
GPT teacher head0.288
Teacher spread0.272 · 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 designNot applicable
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

Citations8
Published2018
Admission routes1
Has abstractyes

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