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Record W2121406383 · doi:10.3201/eid1601.081758

Methicillin-Resistant and -Susceptible<i>Staphylococcus aureus</i>Infections in Dogs

2009· article· en· W2121406383 on OpenAlexafffundabout
Meredith C Faires, Michelle Traverse, Kathy C. Tater, David L. Pearl, J. Scott Weese

Bibliographic record

VenueEmerging infectious diseases · 2009
Typearticle
Languageen
FieldMedicine
TopicAntimicrobial Resistance in Staphylococcus
Canadian institutionsUniversity of Guelph
FundersOntario Veterinary College, University of Guelph
KeywordsMedicineStaphylococcus aureusMethicillin-resistant Staphylococcus aureusOdds ratioAntimicrobialStaphylococcal infectionsInternal medicineInfection controlIntensive care medicineMicrobiologyBiology

Abstract

fetched live from OpenAlex

Methicillin-resistant Staphylococcus aureus (MRSA) has become a pathogen of animals. To compare types of infections, clinical outcomes, and risk factors associated with MRSA in dogs with those associated with methicillin-susceptible Staphylococcus aureus (MSSA) infections, we conducted a case-control study at 3 veterinary referral hospitals in the United States and Canada during 2001-2007. Risk factors analyzed were signalment, medical and surgical history, and infection site. Among 40 dogs with MRSA and 80 with MSSA infections, highest prevalence of both infections was found in skin and ears. Although most (92.3%) dogs with MRSA infections were discharged from the hospital, we found that significant risk factors for MRSA infection were receipt of antimicrobial drugs (odds ratio [OR] 3.84, p = 0.02), Beta-lactams (OR 3.58, p = 0.04), or fluoroquinolones (OR 5.34, p = 0.01), and intravenous catheterization (OR 3.72, p = 0.02). Prudent use of antimicrobial drugs in veterinary hospitals is advised.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.010
GPT teacher head0.283
Teacher spread0.273 · 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.

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

Citations74
Published2009
Admission routes3
Has abstractyes

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