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Record W2791561244 · doi:10.1093/femsle/fny026

Diversification of Pseudomonas aeruginosa within the cystic fibrosis lung and its effects on antibiotic resistance

2018· review· en· W2791561244 on OpenAlexafffund
Shawn T. Clark, David S. Guttman, David Hwang

Bibliographic record

VenueFEMS Microbiology Letters · 2018
Typereview
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsUniversity of TorontoToronto General HospitalCanada Research ChairsUniversity Health Network
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsPseudomonas aeruginosaCystic fibrosisAntibiotic resistanceBiologyAntibioticsMicrobiologyAntimicrobialGenetic diversityBacteriaGeneticsMedicinePopulation

Abstract

fetched live from OpenAlex

The evolution and diversification of bacterial pathogens within human hosts represent potential barriers to the diagnosis and treatment of life-threatening infections. Tremendous genetic and phenotypic diversity is characteristic of host adaptation in strains of Pseudomonas aeruginosa that infect the airways of individuals with chronic lung diseases and prove to be extremely difficult to eradicate. In this MiniReview, we examine recent advances in understanding within-host diversity and antimicrobial resistance in P. aeruginosa populations from the lower airways of individuals with the fatal genetic disease cystic fibrosis and the potential impacts that this diversity may have on detecting and interpreting antimicrobial susceptibility within these populations.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.018
GPT teacher head0.299
Teacher spread0.280 · 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 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

Citations34
Published2018
Admission routes2
Has abstractyes

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