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Decontamination Strategies for Control of Environmental Clostridium difficile (Decontamination and C. difficile)

2014· article· en· W1991895473 on OpenAlexvenueno aff
Chetana Vaishnavi

Bibliographic record

VenueGlobal Journal of Pathology and Microbiology · 2014
Typearticle
Languageen
FieldMedicine
TopicClostridium difficile and Clostridium perfringens research
Canadian institutionsnot available
Fundersnot available
KeywordsHuman decontaminationClostridium difficileC difficileMedicineMicrobiologyAntibioticsBiology

Abstract

fetched live from OpenAlex

Clostridium difficile is a global nosocomial pathogen associated with increased morbidity and mortality particularly with the emergence of the hypervirulent strains. C. difficile is ubiquitously present in the environment due to contamination by the excreta of humans and animals. In the hospital environment C. difficile can be isolated from about 30% patients receiving antibiotics or those who are hospitalized. C. difficile spores are not only resistant to antibiotics, but they can also resist the harsh environmental conditions for longer times and thereby facilitate the spread of C. difficile infection (CDI). The primary mode of transmission of the disease is via the feco-oral route as symptomatic patients shed a large number of the pathogen resulting in contamination of the environmental surfaces. CDI occurs in patients with certain risk factors and who acquire the pathogen by ingestion or via contaminated equipments. In a hospital setting, outbreaks can occur in hospitals, nursing homes, and other extended-care facilities due to C. difficile. Therefore strategies to reduce the contamination of C. difficile in the environment will be of prime importance to every health care facility. In this review, the environmental reservoirs of C. difficile, the transmission and the risk factors of CDI are briefly described and the decontamination strategies for containing the pathogen are elaborated.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.007
GPT teacher head0.257
Teacher spread0.249 · 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 designBench or experimental
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
Published2014
Admission routes1
Has abstractyes

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