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Record W2091786605 · doi:10.1017/s0950268809991488

A comparison of risk factors associated with community-associated methicillin-resistant and -susceptible<i>Staphylococcus aureus</i>infections in remote communities

2010· article· en· W2091786605 on OpenAlexafffund
George R. Golding, Paul N. Levett, Ryan McDonald, James Irvine, Mandiangu Nsungu, Shirley Woods, A. HORBAL, Christine G Siemens, M. A. Khan, Marianna Ofner-Agostini, M. R. Mulvey

Bibliographic record

VenueEpidemiology and Infection · 2010
Typearticle
Languageen
FieldMedicine
TopicAntimicrobial Resistance in Staphylococcus
Canadian institutionsPublic Health Agency of CanadaUniversity of ManitobaSaskatchewan Disease Control Laboratory
FundersCanadian Institutes of Health ResearchPublic Health AgencyPublic Health Agency of Canada
KeywordsMethicillin-resistant Staphylococcus aureusStaphylococcus aureusMicrobiologyMedicineStaphylococcal infectionsBiologyBacteria

Abstract

fetched live from OpenAlex

In this case-control study, cases [community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA), n=79] and controls [community-associated methicillin-susceptible S. aureus (CA-MSSA), n=36] were defined as a laboratory-confirmed infection in a patient with no previous hospital-associated factors. Skin and soft tissue were the predominant sites of infection, both for cases (67.1%) and controls (55.6%). Most of the cases (79.7%) and controls (77.8%) were aged <30 years. Investigations did not reveal any significant statistical differences in acquiring a CA-MRSA or CA-MSSA infection. The most common shared risk factors included overcrowding, previous antibiotic usage, existing skin conditions, household exposure to someone with a skin condition, scratches/insect bites, and exposure to healthcare workers. Similar risk factors, identified for both CA-MRSA and CA-MSSA infections, suggest standard hygienic measures and proper treatment guidelines would be beneficial in controlling both CA-MRSA and CA-MSSA in remote communities.

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)0.004
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.008
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 integrity0.0010.003
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 head0.067
GPT teacher head0.355
Teacher spread0.288 · 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

Citations32
Published2010
Admission routes2
Has abstractyes

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