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Record W2229879192 · doi:10.1155/2015/921712

A new Era of Personalized Medicine for Cystic Fibrosis – at Last!

2015· review· en· W2229879192 on OpenAlexafffund
Bradley S. Quon, Pearce Wilcox

Bibliographic record

VenueCanadian Respiratory Journal · 2015
Typereview
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
FundersUniversity of British ColumbiaCystic Fibrosis Canada
KeywordsCystic fibrosisMedicinePersonalized medicineCystic fibrosis transmembrane conductance regulatorDrug discoveryMutationFunction (biology)Computational biologyBioinformaticsDrug developmentDrugGenePharmacologyInternal medicineGeneticsBiology

Abstract

fetched live from OpenAlex

The gene responsible for cystic fibrosis (CF) was discovered 25 years ago. This breakthrough has enabled a sophisticated understanding of how various mutations lead to specific alterations in the structure and function of the CF transmembrane regulator (CFTR) protein. Until recently, all therapies in CF were focused on ameliorating the downstream consequences of CFTR dysfunction. High-throughput drug screening approaches have yielded compounds that can modify CFTR structure and function, thus targeting the basic defect in CF. The present article describes the CFTR mutational classes, reviews mutation-specific therapies currently in late-phase clinical development, and highlights research opportunities and challenges with personalized medicine in CF.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.713
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
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)0.0020.000

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 head0.087
GPT teacher head0.385
Teacher spread0.298 · 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

Citations9
Published2015
Admission routes2
Has abstractyes

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