No one left behind: review of precision medicine and cystic fibrosis—how the changing approach to cystic fibrosis treatment might lead to tailored therapies for all
Bibliographic record
Abstract
Cystic fibrosis is an autosomal recessive, multisystem disorder that has been historically associated with poor life expectancy.Due to the defective cystic fibrosis transmembrane conductance regulator protein, patients with cystic fibrosis develop viscous secretions that are difficult to clear, resulting in numerous abnormalities such as chronic airway obstruction, maldigestion and malabsorption.While our understanding of the pathophysiology and disease management have improved, pulmonary disease remains the leading cause of morbidity and mortality in patients with cystic fibrosis.However, since the introduction of precision medicine, novel therapeutic agents have been developed to target the underlying defective protein, resulting in improved disease management and life expectancy.The goal of precision medicine is to provide timely diagnosis, phenotyping, and personalized treatments, based on an individualized analysis of a patient's genome.This article reviews current and potential precision medicine treatments for patients with cystic fibrosis, including cystic fibrosis transmembrane conductance regulator modulators and other modulators designed for patients who would not benefit from currently available therapies.We will also discuss other investigational treatment modalities, such as ribosomal read-though agents and RNA therapy, which may continue the advancement of cystic fibrosis treatment.Current research into methods aimed to better predict patients' responses to personalized treatment, such as theratyping, will also be discussed.Given the benefits of applying precision medicine in cystic fibrosis, future research in this therapeutic approach will also likely benefit other life-threatening monogenetic disorders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".