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Detection, treatment, and prevention of carbapenemase-producing<i>Enterobacteriaceae</i>: Recommendations from an International Working Group

2013· article· en· W2104290226 on OpenAlexaff
Gabriel Levy Hara, Ian M. Gould, Andrea Endimiani, Pilar Ramón-Pardo, George L. Daikos, Po‐Ren Hsueh, Shaheen Mehtar, G. Petrikkos, José María Casellas, Lucía Daciuk, Daniela Paciel, Andrea Novelli, Raphael Saginur, Daniel Pryluka, Julio C. Medina, Eduardo Savio

Bibliographic record

VenueJournal of Chemotherapy · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsEtestColistinBroth microdilutionCarbapenemCarbapenem-resistant enterobacteriaceaeAgar dilutionMinimum inhibitory concentrationMedicineEnterobacteriaceaeRegimenAntimicrobialMicrobiologyAntibioticsAminoglycosideInternal medicinePharmacologyBiology

Abstract

fetched live from OpenAlex

The prevalence of carbapenemase-producing Enterobacteriaceae (CPE) has increased during the past 10 years. Its detection is frequently difficult, because they do not always show a minimum inhibitory concentration (MIC) value for carbapenems in the resistance range. Both broth microdilution and agar dilution methods are more sensitive than disk diffusion method, Etest and automated systems. Studies on antimicrobial treatment are based on a limited number of patients; therefore, the optimal treatment is not well established. Combination therapy with two active drugs appears to be more effective than monotherapy. Combination of a carbapenem with another active agent--preferentially an aminoglycoside or colistin--could lower mortality provided that the MIC is ≤4 mg/l and probably ≤8 mg/l, and is administered in a higher-dose/prolonged-infusion regimen. An aggressive infection control and prevention strategy is recommended, including reinforcement of hand hygiene, using contact precautions and early detection of CPE through use of targeted surveillance.

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)0.000
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 score0.394
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.013
GPT teacher head0.277
Teacher spread0.264 · 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

Citations72
Published2013
Admission routes1
Has abstractyes

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