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Record W2166136528 · doi:10.1093/jac/dkt021

Introduction to the CANWARD study (2007-11)

2013· article· en· W2166136528 on OpenAlexafffundabout
D. J. Hoban, George G. Zhanel

Bibliographic record

VenueJournal of Antimicrobial Chemotherapy · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsUniversity of ManitobaShared HealthHealth Sciences Centre
FundersAstellas PharmaBayer CanadaMedicines CompanyAstraZenecaUniversity of ManitobaSunovionPfizerPfizer CanadaAchaogenAbbott Laboratories
KeywordsMedicineAntibiotic resistanceAntimicrobialHealth careIntensive care medicineResistance (ecology)Family medicineMedical emergencyAntibioticsBiology

Abstract

fetched live from OpenAlex

Antimicrobial resistance is a continuing challenge to the appropriate, timely, cost-effective and ecologically appropriate delivery of antimicrobials. Surveillance studies conducted globally and nationally can assist in determining trends over specific geographical areas and allow comparisons between countries and within very specific infectious processes. CANWARD is an ongoing, national, Health Canada-endorsed, multiyear study focused on both inpatient and outpatient pathogens isolated in Canadian hospitals and determining their degree of antimicrobial resistance, with a particular emphasis on specific wards (medical, surgical, intensive care units, emergency room and clinics) as well as specific infection sites (urine, blood, respiratory and wound). This Supplement documents the initial 5 years of the CANWARD study (2007–11 inclusive). The seven manuscripts in this Supplement provide a comprehensive examination of pathogens covering multiple infectious processes. The data highlight the continued emergence of resistance and should provide to healthcare professionals nationally and globally the current status of antimicrobial resistance in Canada.

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)0.018
metaresearch head score (Gemma)0.030
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.616
Threshold uncertainty score0.772

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.008
Science and technology studies0.0050.001
Scholarly communication0.0070.001
Open science0.0030.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0610.019

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 head0.005
GPT teacher head0.235
Teacher spread0.230 · 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
GenreEditorial

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

Citations16
Published2013
Admission routes3
Has abstractyes

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