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Record W2014338056 · doi:10.1055/s-0029-1238917

The Approach to<i>Pseudomonas aeruginosa</i>in Cystic Fibrosis

2009· review· en· W2014338056 on OpenAlexaff
Glenda N. Bendiak, Félix Ratjen

Bibliographic record

VenueSeminars in Respiratory and Critical Care Medicine · 2009
Typereview
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsPseudomonas aeruginosaMedicineCystic fibrosisChronic infectionAntibioticsIntensive care medicineImmunologyOrganismDiseaseMicrobiologyBacteriaImmune systemPathologyInternal medicineBiology

Abstract

fetched live from OpenAlex

Pseudomonas aeruginosa continues to be the most common pathogen in cystic fibrosis (CF) lung disease, and chronic infection with mucoid strains is associated with an accelerated decline in lung function. Although multiple factors can potentially explain the susceptibility of CF airways to this organism, their individual relevance is still largely unclear. Prevention of infection remains an important task, and hygiene measures have been successful in reducing cross-infection, but the universal presence of the organism creates an ongoing challenge, and vaccination strategies have not been highly successful to date. Over the last decade treatment strategies have shifted from controlling chronic infection to attempting to eradicate P. aeruginosa in the early stages of infection. Multiple strategies have been shown to be efficacious, but the optimal form and duration of therapy have yet to be defined. Inhaled antibiotics are a key component of maintenance therapy for chronic infection, and the spectrum of available compounds is rapidly expanding. Pulmonary exacerbations can be reduced with this strategy but usually require intravenous antibiotic therapy once they occur. Nonantibiotic approaches to address P. aeruginosa infection are currently being developed and may expand the therapeutic repertoire in the future.

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)0.002
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.969
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.043
GPT teacher head0.390
Teacher spread0.347 · 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 designOther design
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
Published2009
Admission routes1
Has abstractyes

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