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Record W2944587622 · doi:10.1093/cid/ciz364

Reconciling Antimicrobial Susceptibility Testing and Clinical Response in Antimicrobial Treatment of Chronic Cystic Fibrosis Lung Infections

2019· article· en· W2944587622 on OpenAlexaff
Valerie J. Waters, Timothy J. Kidd, Rafael Cantón, Miquel B. Ekkelenkamp, Helle Krogh Johansen, John J. LiPuma, Scott C. Bell, J.S. Elborn, Patrick A. Flume, Donald R. VanDevanter, Peter H. Gilligan, Wendy Bullington, Pierre‐Régis Burgel, Catherine A. Byrnes, Pavel Dřevı́nek, Alison Holmes, Barbara C. Kahl, Holly Maples, Stacey L. Martiniano, Susanna A. McColley, Andrew M. Morris, Marianne S. Muhlebach, Michael D. Parkins, Félix Ratjen, Jason A. Roberts, Lisa Saiman, Anand Shah, Alan R Smyth, Ranjani Somayaji, Giovanni Taccetti, Michael M. Tunney, Kevin Winthrop, Edith T. Zemanick

Bibliographic record

VenueClinical Infectious Diseases · 2019
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersNational Institute for Health and Care Research
KeywordsMedicineBronchiectasisCystic fibrosisAntimicrobialAntibiotic resistanceAntibioticsLungImmunologyIntensive care medicineInternal medicineMicrobiologyBiology

Abstract

fetched live from OpenAlex

Median cystic fibrosis (CF) survival has increased dramatically over time due to several factors, including greater availability and use of antimicrobial therapies. During the progression of CF lung disease, however, the emergence of multidrug antimicrobial resistance can limit treatment effectiveness, threatening patient longevity. Current planktonic-based antimicrobial susceptibility testing lacks the ability to predict clinical response to antimicrobial treatment of chronic CF lung infections. There are numerous reasons for these limitations including bacterial phenotypic and genotypic diversity, polymicrobial interactions, and impaired antibiotic efficacy within the CF lung environment. The parallels to other chronic diseases such as non-CF bronchiectasis are discussed as well as research priorities for moving forward.

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)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.038
GPT teacher head0.392
Teacher spread0.354 · 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 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

Citations77
Published2019
Admission routes1
Has abstractyes

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