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Record W2053815280 · doi:10.1016/s0001-2092(06)60418-3

Evidenced‐based practice for control of methicillin‐resistant <i>Staphylococcus aureus</i>

2005· review· en· W2053815280 on OpenAlexaffabout
Marilyn Ott, Jing Shen, Sue Sherwood

Bibliographic record

VenueAORN Journal · 2005
Typereview
Languageen
FieldMedicine
TopicInfection Control in Healthcare
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHygieneInfection controlIsolation (microbiology)MedicineMethicillin-resistant Staphylococcus aureusStaphylococcus aureusDisease controlCross infectionIntensive care medicineClinical PracticeNursingMicrobiologyEnvironmental healthPathologyBiologyBacteria

Abstract

fetched live from OpenAlex

The increasing prevalence of methicillin-resistant Staphylococcus aureus (MRSA) has become a global issue and affects nursing practice in many clinical areas. This article explores methods for effective control of MRSA in hospital settings. Based on infection control guidelines provided by the Centers for Disease Control and Prevention, the College of Nurses of Ontario, AORN, the World Health Organization, and several evidence-based studies, strategies for MRSA infection control measures include hand hygiene, contact isolation, and hospital environment hygiene.

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)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.093
GPT teacher head0.449
Teacher spread0.356 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations5
Published2005
Admission routes2
Has abstractyes

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