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Record W4366083909 · doi:10.1128/aem.00105-23

Serratia marcescens Colonization in a Neonatal Intensive Care Unit Has Multiple Sources, with Sink Drains as a Major Reservoir

2023· article· en· W4366083909 on OpenAlexafffund
Thibault Bourdin, Marie-Ève Benoit, Alizée Monnier, Émilie Bédard, Michèle Prévost, Dominique Charron, Nathalie Audy, Sophie Gravel, Mélanie Sicard, Caroline Quach, Éric Déziel, Philippe Constant

Bibliographic record

VenueApplied and Environmental Microbiology · 2023
Typearticle
Languageen
FieldMedicine
TopicNeonatal and Maternal Infections
Canadian institutionsUniversité de MontréalPolytechnique MontréalCentre Hospitalier Universitaire Sainte-JustineInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchGovernment of CanadaPolytechnique MontréalCentre hospitalier universitaire Sainte-JustineAix-Marseille UniversitéCalifornia HIV/AIDS Research Program
KeywordsSerratia marcescensSink (geography)ColonizationOutbreakNeonatal intensive care unitIntensive careBiologyMicrobiologyMedicineIntensive care medicinePediatricsVirologyGeographyEscherichia coli

Abstract

fetched live from OpenAlex

The bacterium Serratia marcescens is an important opportunistic human pathogen that thrives in many environments, can become multidrug resistant, and is often involved in nosocomial outbreaks in neonatal intensive care units (NICU). We evaluated the role of sinks during five suspected S. marcescens outbreaks in a NICU. An innovative approach combining molecular and culture methods was used to maximize the detection and typing of S. marcescens in the sink environment. Our results indicate multiple reservoirs of S. marcescens within the NICU, including patients, sink drains, and external sources. These results highlight the importance of sinks as a major reservoir of S. marcescens and potential sources of future outbreaks.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.215
Teacher spread0.203 · 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

Citations37
Published2023
Admission routes2
Has abstractyes

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