Bibliographic record
Abstract
PURPOSE OF REVIEW: Improved knowledge of the molecular mechanisms underlying cystic fibrosis has led to a wide spectrum of new treatment approaches. This review addresses recent therapeutic strategies that either target the underlying defect or early steps in cystic fibrosis pathophysiology. RECENT FINDINGS: While gene therapy does not appear to be a therapeutic option in the near future, cystic fibrosis transmembrane regulator pharmacotherapy is currently being developed as an alternative to reduce cystic fibrosis transmembrane regulator degradation or improve its function. Two drugs that increase chloride secretion via an alternative chloride channel, Moli1901 and denufosol, have been shown to be safe in clinical studies that also suggested clinical efficacy. Osmotic therapy may be an alternative approach to increase airway surface liquid and is being studied as an early intervention strategy. SUMMARY: The spectrum of treatments for cystic fibrosis lung disease is rapidly increasing. While clinical efficacy for most of the compounds still has to be proven in large clinical trials, there is considerable hope that cystic fibrosis therapy will move from addressing the downstream sequelae of the cystic fibrosis transmembrane regulator defect to a more causal approach in the near future.
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 | 0.001 | 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".