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Record W2418155401 · doi:10.1136/bmj.i859

New and emerging targeted therapies for cystic fibrosis

2016· review· en· W2418155401 on OpenAlexaff
Bradley S. Quon, Steven M. Rowe

Bibliographic record

VenueBMJ · 2016
Typereview
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsIvacaftorCystic fibrosisCystic fibrosis transmembrane conductance regulatorMedicineGenetic enhancementDiseaseBioinformaticsLoss functionGeneComputational biologyBiologyPathologyGeneticsPhenotypeInternal medicine

Abstract

fetched live from OpenAlex

Cystic fibrosis (CF) is a monogenic autosomal recessive disorder that affects about 70,000 people worldwide. The clinical manifestations of the disease are caused by defects in the cystic fibrosis transmembrane conductance regulator (CFTR) protein. The discovery of the CFTR gene in 1989 has led to a sophisticated understanding of how thousands of mutations in the CFTR gene affect the structure and function of the CFTR protein. Much progress has been made over the past decade with the development of orally bioavailable small molecule drugs that target defective CFTR proteins caused by specific mutations. Furthermore, there is considerable optimism about the prospect of gene replacement or editing therapies to correct all mutations in cystic fibrosis. The recent approvals of ivacaftor and lumacaftor represent the genesis of a new era of precision medicine in the treatment of this condition. These drugs are having a positive impact on the lives of people with cystic fibrosis and are potentially disease modifying. This review provides an update on advances in our understanding of the structure and function of the CFTR, with a focus on state of the art targeted drugs that are in development.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.939
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.044
GPT teacher head0.406
Teacher spread0.361 · 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.

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

Citations146
Published2016
Admission routes1
Has abstractyes

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