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Record W2611360626 · doi:10.1002/9781119421344.ch7

Methicillin‐Resistant Staphylococcal Infections

2016· other· en· W2611360626 on OpenAlexaff
J. Scott Weese

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMedicine
TopicAntimicrobial Resistance in Staphylococcus
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsStaphylococcus pseudintermediusTransmission (telecommunications)MedicineInfection controlNoseStaphylococcus aureusMethicillin-resistant Staphylococcus aureusStaphylococcal infectionsStaphylococcusIntensive care medicineMicrobiologySurgeryBiologyBacteria

Abstract

fetched live from OpenAlex

Postsurgical methicillin-resistant staphylococcal (MRS) infections in dogs are typically caused by Staphylococcus pseudintermedius (MRSP) or Staphylococcus aureus (MRSA), two varieties of bacteria that can reside in the nose, mouth, intestines, and skin of healthy dogs and humans. When overall infections are considered, prior antimicrobial exposure has been identified as a risk factor for both MRSA and MRSP versus their methicillin-susceptible counterparts in dogs. Consideration must be given to measures that can be used to reduce the risk of transmission of MRS to owners, other patients, and clinic personnel. Transmission of MRSA and MRSP in veterinary hospitals, particularly surgical facilities, has become a major concern. This chapter provides a detailed description of proper practices for infection control. The key to prevention of hospital-associated transmission of MRS likely involves a good general infection control program.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.026
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

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)0.0260.008

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 head0.018
GPT teacher head0.307
Teacher spread0.289 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations3
Published2016
Admission routes1
Has abstractyes

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Same topicAntimicrobial Resistance in StaphylococcusFrench-language works237,207