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Record W2902060433 · doi:10.1017/s0950268818003114

Self-reported risk factors for having <i>Escherichia coli</i> sequence type 131 or its <i>H</i>30 subclone among US Veterans with a clinical <i>E. coli</i> isolate

2018· article· en· W2902060433 on OpenAlexaff
Amee R. Manges, Paul Thuras, Stephen B. Porter, James R. Johnson

Bibliographic record

VenueEpidemiology and Infection · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEscherichia coli research studies
Canadian institutionsUniversity of British Columbia
FundersOffice of Research and DevelopmentU.S. Department of Veterans Affairs
KeywordsEscherichia coliResidenceMedicineMicrobiologyBiologyEnvironmental healthGeneInternal medicineGeneticsDemography

Abstract

fetched live from OpenAlex

Among 469 US military veterans with an Escherichia coli clinical isolate (2012-2013), we explored healthcare and non-healthcare risk factors for having E. coli sequence type 131 and its H30 subclone (ST131-H30). Overall, 66 (14%) isolates were ST131; 51 (77%) of these were ST131-H30. After adjustment for healthcare-associated factors, ST131 remained positively associated with medical lines and nursing home residence. After adjustment for environmental factors, ST131 remained associated with wild animal contact (positive), meat consumption (negative) and pet cat exposure (negative). Thus, ST131 was associated predominantly with healthcare-associated exposures, while non-ST131 E. coli were associated with some environmental exposures.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.066
Threshold uncertainty score0.887

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.074
GPT teacher head0.372
Teacher spread0.298 · 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

Citations8
Published2018
Admission routes1
Has abstractyes

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