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Record W4255468382 · doi:10.14745/ccdr.v46i05a01

Healthcare-associated infections and antimicrobial resistance in Canadian acute care hospitals, 2014–2018

2020· article· en· W4255468382 on OpenAlexafffundvenueabout
Canadian Nosocomial Infection Surveillance

Bibliographic record

VenueCanada Communicable Disease Report · 2020
Typearticle
Languageen
FieldMedicine
TopicNosocomial Infections in ICU
Canadian institutionsPublic Health Agency of Canada
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsAntimicrobial stewardshipInfection controlMedicineAntibiotic resistanceMethicillin-resistant Staphylococcus aureusAntimicrobialHealth careDrug resistanceVancomycinStaphylococcus aureusIntensive care medicineMicrobiologyAntibioticsBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Healthcare-associated infections (HAIs) and antimicrobial resistance (AMR) pose serious threats to the health of Canadians due to increased morbidity, mortality and healthcare costs. Epidemiologic and laboratory surveillance data, collected through the Canadian Nosocomial Infection Surveillance Program, are used to inform infection prevention and control and antimicrobial stewardship programs and policies. The objective of this study was to describe the epidemiologic and laboratory characteristics and trends of HAIs and AMR from 2014 to 2018 using surveillance data provided by Canadian hospitals participating in the Canadian Nosocomial Infection Surveillance Program. METHODS: antibiogram data were collected and are described. RESULTS: =0.014). CONCLUSION: Ongoing efforts to prevent HAIs and reduce AMR in Canada require consistent, standardized surveillance data from acute care hospitals. Increased collaboration with provincial, territorial and international partners in infection prevention and control, as well as antimicrobial stewardship, will be essential in reducing the burden of observed HAIs (including 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.266
Teacher spread0.255 · 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 teacher head, not a consensus.

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

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

Citations28
Published2020
Admission routes4
Has abstractyes

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