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Record W1955772086 · doi:10.5430/jha.v4n6p31

Staphylococcus aureus contamination of environmental surfaces and efficacy of alcohol wiping once daily in a hospital with a long-term care facility

2015· article· en· W1955772086 on OpenAlexvenueno aff
Kumiko Maeda, Shigeharu Oie, Hiroyuki Furukawa

Bibliographic record

VenueJournal of Hospital Administration · 2015
Typearticle
Languageen
FieldMedicine
TopicInfection Control in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsContaminationMedicineStaphylococcus aureusWheelchairSurgeryBiology

Abstract

fetched live from OpenAlex

We evaluated Staphylococcus aureus contamination of door and wheelchair handles in a hospital with a care facility. In the hospital, 11 (27.5%) of 40 door handle sites and 7 (28.0%) of 25 wheelchair handle sites were contaminated. The S. aureus contamination density (mean ± SD) was 9.8 ± 14.0 colony-forming units (cfu) for door handles and 285.0 ± 731.6 cfu for wheelchair handles. In the long-term care facility, 18 (51.4%) of 35 door handle sites and 9 (36.0%) of 25 wheelchair handle sites were contaminated. The S. aureus contamination density was 215.3 ± 657.5 cfu for door handles and 295.7 ± 702.0 cfu for wheelchair handles. Because S. aureus contamination was frequently observed not only in the hospital but also in the care facility, we performed an evaluation to determine whether disinfection by wiping with alcohol once daily was effective for maintaining the cleanliness of door handles. S. aureus contamination was compared between door handles 24 hours after disinfection by wiping with 80% (v/v) ethanol once daily for 5 consecutive days (disinfection group) and door handles not disinfected for 5 days following a single disinfection with 80% (v/v) ethanol (nondisinfection group). The S. aureus level did not differ significantly between the disinfection and nondisinfection groups. Disinfection by wiping with alcohol at 24-hour intervals was not always effective in maintaining the cleanliness of door handles.

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)0.000
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 score0.033
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.015
GPT teacher head0.290
Teacher spread0.275 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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