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Record W2125329883 · doi:10.1586/14787210.4.5.807

Multidrug–resistant organisms in cystic fibrosis: management and infection–control issues

2006· review· en· W2125329883 on OpenAlexaff
Valerie Waters, Félix Ratjen

Bibliographic record

VenueExpert Review of Anti-infective Therapy · 2006
Typereview
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsStenotrophomonas maltophiliaCystic fibrosisPseudomonas aeruginosaMultiple drug resistanceBurkholderiaInfection controlAntibioticsMedicineAntibiotic resistanceStaphylococcus aureusStenotrophomonasDiseaseMicrobiologyChronic infectionBiologyImmunologyIntensive care medicinePseudomonasInternal medicineBacteria

Abstract

fetched live from OpenAlex

Chronic infection and inflammation are the hallmarks of cystic fibrosis lung disease. As cystic fibrosis patients are living longer owing to more intense treatment, multidrug-resistant organisms are being isolated increasingly from patients' respiratory tracts. While the adverse effects of Pseudomonas aeruginosa and Burkholderia cepacia complex are well described, less is known about the clinical significance of other emerging multidrug-resistant organisms, such as methicillin-resistant Staphylococcus aureus and Stenotrophomonas maltophilia. Owing to multiple mechanisms of antimicrobial resistance, these organisms are difficult to treat and often require combination antibiotic therapy. Until more is known about their pathogenicity and effect on clinical outcomes, physicians should be aware of the potential transmissibility of these organisms and implement adequate infection control strategies.

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)0.002
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
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.002
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 head0.020
GPT teacher head0.380
Teacher spread0.360 · 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

Citations49
Published2006
Admission routes1
Has abstractyes

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