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Record W2073839872 · doi:10.1111/jvim.12032

Bacterial Contamination of Stethoscope Chest Pieces and the Effect of Daily Cleaning

2013· article· en· W2073839872 on OpenAlexaff
Hiroshi Fujita, Bernie Hansen, Rita M. Hanel

Bibliographic record

VenueJournal of Veterinary Internal Medicine · 2013
Typearticle
Languageen
FieldMedicine
TopicInfection Control in Healthcare
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsStethoscopeMedicineMicrobiological cultureContaminationBacteriaToxicologyBiologyEcology

Abstract

fetched live from OpenAlex

BACKGROUND: Stethoscopes are a potential source of nosocomial infection for hospitalized humans, a phenomenon not previously studied in companion animals. OBJECTIVES: To determine if daily cleaning of stethoscope chest pieces reduces bacterial contamination between cleanings. ANIMALS: Client-owned dogs and cats. METHODS: Prospective observational study. In phase 1, bacterial cultures were obtained from the chest pieces of 10 participant stethoscopes once weekly for 3 weeks. In phase 2, stethoscopes were cleaned daily and 2 culture samples were obtained once weekly, immediately before and after cleaning with 70% isopropyl alcohol, for 3 weeks. RESULTS: Daily cleaning eliminated bacteria immediately after each cleaning (P = .004), but did not reduce the rate of positive cultures obtained before cleaning in phase 2. Cultures were positive for 20/30 (67%) samples during phase 1 and 18/30 (60%) obtained before daily cleaning during phase 2. Recovered organisms included normal skin flora, agents of opportunistic infections, and potential pathogens. The only genus that was repeatedly recovered from the same stethoscope for 2 or more consecutive weeks was Bacillus sp. CONCLUSIONS AND CLINICAL IMPORTANCE: Daily cleaning was highly effective at removing bacteria, but provided no reduction in precleaning contamination. Cleaning stethoscopes after use on dogs or cats infected with pathogenic bacteria and before use on immunocompromised animals should be considered.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.604
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.022
GPT teacher head0.335
Teacher spread0.313 · 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

Citations11
Published2013
Admission routes1
Has abstractyes

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