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Record W2026959104 · doi:10.1517/14728214.2014.882316

Emerging drugs for cystic fibrosis

2014· review· en· W2026959104 on OpenAlexaff
Reshma Amin, Félix Ratjen

Bibliographic record

VenueExpert Opinion on Emerging Drugs · 2014
Typereview
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsCystic fibrosisMedicineCystic fibrosis transmembrane conductance regulatorDiseaseRegulatorGenetic enhancementBioinformaticsPathologyInternal medicineGeneGeneticsBiology

Abstract

fetched live from OpenAlex

INTRODUCTION: Cystic fibrosis is an autosomal recessive disease, which is the result of a genetic defect in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. Pulmonary disease accounts for over 90% of the morbidity and mortality associated with the disease. Conventionally, CF treatment has focused on symptomatic therapy. AREAS COVERED: In the past, the emphasis for the development of CF therapeutics has previously been on addressing complications of the manifestations rather than on the underlying disease process. However, in the past few decades there has been a paradigm shift with new attention on the underlying biological mechanisms and therapies targeted at curing the disease rather than simply controlling it. This review summarizes the current CF therapeutics pipeline. These developing therapies include CFTR gene therapy, CFTR pharmacotherapeutics, osmotically active agents and anti-inflammatory therapies, as well as novel inhaled antibiotics. EXPERT OPINION: The CF therapeutics pipeline currently holds great promise both for novel therapies directly targeting the underlying biological mechanisms of CFTR dysfunction and new symptomatic therapies. While CFTR-directed therapy has the highest potential to improve patients' outcome, it is important to continue to develop better treatment options for all aspects of CF lung disease.

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)0.002
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 score0.920
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.040
GPT teacher head0.412
Teacher spread0.373 · 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

Citations31
Published2014
Admission routes1
Has abstractyes

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