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Record W4312221787 · doi:10.1016/j.eclinm.2022.101781

Antimicrobial resistance and mortality following E. coli bacteremia

2022· article· en· W4312221787 on OpenAlex
Nick Daneman, Daniel Fridman, Jennie Johnstone, Bradley J. Langford, Samantha Lee, Derek M MacFadden, Kwadwo Mponponsuo, Samir Patel, Kevin L. Schwartz, Kevin A. Brown

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEClinicalMedicine · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsOttawa HospitalHealth Sciences CentrePublic Health OntarioUniversity of TorontoInstitute for Clinical Evaluative SciencesSinai Health SystemSunnybrook Health Science Centre
FundersCanadian Institutes of Health ResearchMinistry of Health -SingaporeOntario Ministry of Health and Long-Term CareInstitute for Clinical Evaluative Sciences
KeywordsMedicineCephalosporinOdds ratioInternal medicineBacteremiaDrug resistanceAntibioticsAntibiotic resistanceAntimicrobialLogistic regressionMicrobiologyBiology

Abstract

fetched live from OpenAlex

Background: Global estimates suggest millions of deaths annually are associated with antimicrobial resistance (AMR) but these are generated from scarce data on the relative risk of death attributable to drug-resistant versus drug-sensitive infections. Methods: bloodstream infection in Ontario, Canada between 2017 and 2020, and measured 90 day mortality among those with resistant versus sensitive isolates for each of 8 commonly used antibiotic classes and a category of difficult to treat resistance (DTTR). We used multivariable logistic regression to calculate an adjusted odds of mortality associated with AMR, after accounting for patient demographics, comorbidities, and prior healthcare exposure. Findings: bloodstream infection, resistance was most common to aminopenicillins (46.8%), followed by first generation cephalosporins (38.8%), fluoroquinolones (26.5%), sulfonamides (24.1%), third generation cephalosporins (13.8%), aminoglycosides (11.7%), beta-lactam-beta-lactamase-inhibitors (9.1%) and carbapenems (0.2%). Only 18 (0.1%) episodes exhibited DTTR. For each antibiotic class, the unadjusted odds of mortality (OR) were higher among resistant isolates, but after accounting for patient characteristics the adjusted odds (aOR) of mortality were attenuated: aminopenicillins (OR 1.22, 95% CI 1.12-1.33; aOR 1.09, 95% CI 0.99-1.20), first generation cephalosporins (OR 1.24, 95% CI 1.14-1.35; aOR 1.07, 95% CI 0.97-1.18), third generation cephalosporins (OR 1.64, 95% CI 1.47-1.82; aOR 1.29, 95% CI 1.15-1.46), beta-lactam-beta-lactamase-inhibitors (OR 1.69, 95% CI 1.52-1.89, aOR 1.28, 95% CI 1.13-1.45), carbapenems (OR 3.11, 95% CI 1.52-6.34; aOR 2.06, 95% CI 0.91-4.66), sulfonamides (OR 1.19, 95% CI 1.07-1.31, aOR 1.06, 95% CI 0.95-1.18), fluoroquinolones (OR 1.49, 95% CI 1.36-1.64, aOR 1.16, 95% CI 1.05-1.29), aminoglycosides (OR 1.43, 95% CI 1.27-1.62; aOR 1.27, 95% CI 1.11-1.46), and DTTR (OR 3.71, 95% CI 1.46-9.41; aOR 2.58, 95% CI 0.87-7.66). Interpretation: bloodstream infection, particularly for resistance to classes commonly used as empiric treatment. Surveillance for AMR-associated mortality should incorporate adjustment for patient characteristics and prior healthcare utilization. Funding: This work was supported by a project grant from CIHR (grant number 159503). This study was also supported by ICES, which is funded by an annual grant from Ontario Ministry of Health and Long-Term Care (MOHLTC).

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.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.330
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.019
GPT teacher head0.310
Teacher spread0.291 · 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