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Record W2620585532 · doi:10.1055/s-0037-1602583

Emergence of Antimicrobial Resistance among Pseudomonas aeruginosa: Implications for Therapy

2017· review· en· W2620585532 on OpenAlexaff
George G. Zhanel, Nina M. Clark, Joseph P. Lynch

Bibliographic record

VenueSeminars in Respiratory and Critical Care Medicine · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicinePseudomonas aeruginosaAntimicrobialPneumoniaAntibiotic resistanceIntensive care medicineVentilator-associated pneumoniaHospital-acquired pneumoniaImmunologyAntibioticsMicrobiologyInternal medicineBacteriaBiology

Abstract

fetched live from OpenAlex

Pseudomonas aeruginosa (PA), a nonlactose fermenting gram-negative bacillus, is a common cause of nosocomial infections in critically ill or debilitated patients, particularly ventilator-associated pneumonia (VAP), and infections of bloodstream, urinary tract, intra-abdominal, wounds/skin/soft tissue. PA rarely affects healthy individuals, but may cause serious infections in patients with chronic structural lung disease, comorbidities, advanced age, impaired immune defenses, or with medical devices (e.g., urinary or intravascular catheters, foreign bodies). Treatment of pseudomonal infections is difficult, as PA is intrinsically resistant to multiple antimicrobials, and may acquire new resistance determinants even while on antimicrobial therapy. Mortality associated with pseudomonal VAP or bacteremias is high (> 35%) and optimal therapy is controversial. Over the past three decades, antimicrobial resistance among PA has escalated globally, via dissemination of several international multidrug-resistant “epidemic” clones. We review the emergence of antimicrobial resistance to this pathogen, and discuss approaches to therapy (both empirical and definitive).

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.960
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.068
GPT teacher head0.405
Teacher spread0.337 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations43
Published2017
Admission routes1
Has abstractyes

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