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Record W6958895539 · doi:10.6084/m9.figshare.c.4856592

Antimicrobial use among adult inpatients at hospital sites within the Canadian Nosocomial Infection Surveillance Program: 2009 to 2016

2020· other· en· W6958895539 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2020
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicPasture and Agricultural Systems
Canadian institutionsnot available
Fundersnot available
KeywordsAntimicrobialAntibiotic resistanceRetrospective cohort studyAntibioticsInfection controlDrug resistanceMEDLINE

Abstract

fetched live from OpenAlex

Abstract Background Antimicrobial resistance is a growing threat to the world’s ability to prevent and treat infections. Links between quantitative antibiotic use and the emergence of bacterial resistance are well documented. This study presents benchmark antimicrobial use (AMU) rates for inpatient adult populations in acute-care hospitals across Canada. Methods In this retrospective surveillance study, acute-care adult hospitals participating in the Canadian Nosocomial Infection Surveillance Program (CNISP) submitted annual AMU data on all systemic antimicrobials from 2009 to 2016. Information specific to intensive care units (ICUs) and non-ICU wards were available for 2014–2016. Data were analyzed using defined daily doses (DDD) per 1000 patient days (DDD/1000pd). Results Between 2009 and 2016, 16–18 CNISP adult hospitals participated each year and provided their AMU data (22 hospitals participated in ≥1 year of surveillance; 11 in all years). From 2009 to 2016, there was a significant reduction in use (12%) (from 654 to 573 DDD/1000pd, p = 0.03). Fluoroquinolones accounted for the majority of this decrease (47% reduction in combined oral and intravenous use, from 129 to 68 DDD/1000pd, p

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.699
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0180.003

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 head0.017
GPT teacher head0.201
Teacher spread0.184 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2020
Admission routes1
Has abstractyes

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