MétaCan
Menu
Back to cohort
Record W2588434314 · doi:10.1093/ndt/gfw170.17

SP410EFFECT OF BIOFILM FORMATION ON HEMODIALYSIS MONITOR DISINFECTION

2016· article· en· W2588434314 on OpenAlexaff
L. Sereni, Marialuisa Caiazzo, Silvia Mafredini, Mary Lou Wratten, Giuseppe Palladino

Bibliographic record

VenueNephrology Dialysis Transplantation · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsBell (Canada)
Fundersnot available
KeywordsMedicineBiofilmHemodialysisIntensive care medicineMicrobiologySurgeryBacteria

Abstract

fetched live from OpenAlex

Introduction and Aims: Biofilms are composed of communities of microorganisms that adhere to almost any surface. In correct conditions of a hemodialysis apparatus, the probability of biofilm formation within its hydraulic circuit is very low. In particular situations, e.g. long periods of downtime or use of water with a high bacterial content, this probability increases considerably. The aim of this work was to evaluate the combined effect of two different disinfection methods: physical (descaling with citric acid at 70°C) and chemical (disinfection with Oxagal®), on reduction of bacteria contents in a hemodialysis monitor (FLEXYA® Dialysis Machine). In particular, the effectiveness of the disinfection action despite the biofilm presence inside the hydraulic circuit was investigated. Methods: The hydraulic circuit of FLEXYA was intentionally contaminated according two different contamination protocols, A and B described below, using a 108 CFU/ml suspension of Pseudomonas aeruginosa. For the bacteria determination, sample of water volume was aspirated at 0, 24, 48 and 72 h after disinfection from different sampling sites of the hydraulic circuit: on dialysate outlet (before entry in the hemodialyser), immediately after heating tank, after mixing and after the recirculation circuit. Samples were filtered through a 0.45 μm membrane filter. The filter was placed in culture at 32°C for 5 days on Reasoner 2A agar plates. Protocol A: The monitor was contaminated by recirculating 500 ml of the bacterial suspension for 1 hour at 37°C. The hydraulic circuit, washed with deionized water for 5 min at 500 ml/min, was disinfected with the two different procedures: physical followed by a chemical disinfection with 60 hour of stationing. The monitor was finally, turned off and samples were taken at 0, 24, 48 and 72 h of non-use. Protocol B: The monitor was contaminated by recirculating 500 ml of the bacterial suspension for 1 h at 37°C, then switched off leaving the bacterial suspension inside the circuit for 72 h in order to allow biofilm formation. The monitor was washed with deionized water for 5 min at 500 ml/min and later, was disinfected with a physical followed by a chemical disinfection with different time of stationary phase: 60, 36, 24 and 12 hours. For each stationary phase duration, samples were taken at 0, 24, 48 and 72 h of non-use.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.012
GPT teacher head0.232
Teacher spread0.220 · 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 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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueNephrology Dialysis TransplantationSame topicBlood donation and transfusion practicesFrench-language works237,207