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Record W3131329567 · doi:10.1007/s10096-021-04185-7

Molecular characterization of clinical carbapenem-resistant Enterobacterales from Qatar

2021· article· en· W3131329567 on OpenAlexaff
Fatma Ben Abid, Clement K. M. Tsui, Yohei Doi, Anand Deshmukh, Christi L. McElheny, William C. Bachman, Erin L. Fowler, Ahmed Albishawi, Kamran Mushtaq, Emad Bashir Ibrahim, Sanjay H. Doiphode, Manal Hamed, Muna Almaslmani, Abdullatif Al‐Khal, Adeel A. Butt, Ali S. Omrani

Bibliographic record

VenueEuropean Journal of Clinical Microbiology & Infectious Diseases · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthHamad Medical Corporation
KeywordsKlebsiella pneumoniaeBiologyMicrobiologyMedical microbiologyEscherichia coliLocus (genetics)Multilocus sequence typingCarbapenemWhole genome sequencingTypingGeneGeneticsGenomeGenotypeAntibiotics

Abstract

fetched live from OpenAlex

Abstract One hundred forty-nine carbapenem-resistant Enterobacterales from clinical samples obtained between April 2014 and November 2017 were subjected to whole genome sequencing and multi-locus sequence typing. Klebsiella pneumoniae (81, 54.4%) and Escherichia coli (38, 25.5%) were the most common species. Genes encoding metallo-β-lactamases were detected in 68 (45.8%) isolates, and OXA-48-like enzymes in 60 (40.3%). bla NDM-1 (45; 30.2%) and bla OXA-48 (29; 19.5%) were the most frequent. KPC-encoding genes were identified in 5 (3.6%) isolates. Most common sequence types were E. coli ST410 (8; 21.1%) and ST38 (7; 18.4%), and K. pneumoniae ST147 (13; 16%) and ST231 (7; 8.6%).

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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.582
Threshold uncertainty score0.913

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.017
GPT teacher head0.307
Teacher spread0.289 · 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 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

Citations35
Published2021
Admission routes1
Has abstractyes

Explore more

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