Bibliographic record
Abstract
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.013 |
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".