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Record W3119065724 · doi:10.1093/ofid/ofaa439.1004

815. Biofilm Accumulation in New Flexible Gastroscope Channels within 30 Days in Clinical Use

2020· article· en· W3119065724 on OpenAlexaffabout
Mariusa Gomes Borges Primo, Dayane de Melo Costa, Simone Vieira Toledo Guadagnin, Adriana da Silva Azevedo, Michelle J. Alfa, Karen Vickery, Lara Stefânia Netto de Oliveira Leão‐Vasconcelos, Anaclara Ferreira Veiga Tipple

Bibliographic record

VenueOpen Forum Infectious Diseases · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMedical Device Sterilization and Disinfection
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsBiofilmMedicineGlutaraldehydeBiomedical engineeringAir waterContaminationPathologyBacteria

Abstract

fetched live from OpenAlex

Abstract Background Flexible endoscopes are complex-design reusable devices, with long and narrow channels, making reprocessing difficult. Biofilm formation is a key factor for persistent contamination, as it protects microorganism against cleaning and disinfection agents. The aim of this study was to assess the accumulation of biofilm on the inner surfaces of new flexible gastroscope channels after 30 days of patient-use and full reprocessing. Methods Three flexible gastroscopes (FG) (GIF–Q150, OlympusTM) with new internal channels (TeflonTM) were subjected to 30 days of clinical use and reprocessing by trained nursing personnel, using a revised reprocessing protocol, at the endoscopy service of a Brazilian teaching hospital (235 beds). The reprocessing protocol included: pre-cleaning; manual cleaning; automated cleaning and disinfection - 2% Glutaraldehyde; manual drying (forced-air drying) and alcohol rinsing, and storage in vertical position in exclusive cabinets. Then, internal channels were removed from the three patient-ready FG (three biopsy, three air, three water and three air/water junction channels), and the inner surface subjected to bacteriological culture (~30 cm) (n=9) and Scanning Electron Microscopy (SEM) (~1 cm) (n=12). Air/water junctions (~1 cm) were subjected to SEM only. Results The average of use/reprocessing of the FG was 60 times. Bacterial growth was detected in 6/9 channels (three from FG#1 showed residual moisture) and seven bacterial isolates were recovered, most from air or water channels (Fig 1). Inner surface structural damage was identified in 11/12 channels by SEM. Extensive biofilm was detected in air, water and air/water junction channels (7/12) (Fig 2). Residuals matter were detected in all channels (12/12). Fig 1. Distribution of bacterial growth and genera/species identified in new flexible gastroscope channels after 30 days of patient-use and reprocessing at the endoscopy service of a large Brazilian teaching hospital. *FG1: flexible gastroscope nº1 **FG2: flexible gastroscope nº2 ***FG3: flexible gastroscope nº3 ¥Moisture was visually detected inside the channels during longitudinal cutting for SEM. Fig 2. Scanning Electron Micrographs showing extensive biofilm, containing bacilli/rods and/or cocci shape bacteria, on the inner surface of new flexible gastroscope channels after 30 days of patient-use and reprocessing at the endoscopy service of a large Brazilian teaching hospital. *FG1: flexible gastroscope nº1 **FG2: flexible gastroscope nº2 ***FG3: flexible gastroscope nº3 Conclusion The short timeframe before damage and biofilm accumulation in the channels were evident and suggests that improving endoscope design is necessary, while better reprocessing methods and channel maintenance needs to be investigated in detail. Improving design, maintenance and reprocessing of endoscopes will ensure safe use of these devices. Disclosures Michelle J. Alfa, B.Sc., M.Sc., Ph.D, Healthmark (Consultant, Other Financial or Material Support, Royalty monies from University of Manitoba that are provided through a License agreement with Healthmark)Kikkoman (Consultant)Olympus (Consultant, Advisor or Review Panel member, Speaker’s Bureau)STERIS (Consultant, Speaker’s Bureau)

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.089
GPT teacher head0.368
Teacher spread0.279 · 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; both teacher heads agree on what is shown here.

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".

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Citations1
Published2020
Admission routes2
Has abstractyes

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