Expanding the Impact of New Cystic Fibrosis Therapies in Low‐ and Middle‐Income Countries
Bibliographic record
Abstract
BACKGROUND: Cystic fibrosis (CF) primarily affects Caucasian populations, with the highest prevalence in countries like Ireland, the UK, Australia, and Canada. Despite significant improvements in survival, pulmonary insufficiency remains the leading cause of death. Factors such as nutrition, chronic Pseudomonas aeruginosa (PsA) infection, genotype, pancreatic status, and cystic fibrosis-related diabetes affect pulmonary function across age groups. OBJECTIVE: This review examines disparities in CF care and outcomes between high-income countries (HICs) and low-income countries (LICs), focusing on the impact of CFTR modulators like Elexacaftor/Tezacaftor/Ivacaftor (ETI) and challenges in accessing care in LICs. METHODS: Data from the European CF Society Patient Registry and studies on CF outcomes across regions were reviewed to assess survival trends, pulmonary function, and infection rates among people with CF (pwCF). The effects of CFTR modulator therapies, particularly for F508del carriers, were also evaluated. RESULTS: In HICs, improvements in survival rates and pulmonary function have been noted, especially with the use of CFTR modulators like ETI. However, in LICs, challenges like limited access to therapies, delayed diagnosis, poor nutrition, and high PsA infection rates lead to poorer outcomes. In regions with fewer F508del carriers, access to care and medications is further limited, exacerbating disparities. CONCLUSION: Although CF treatment advancements have improved outcomes in many pwCF, these benefits are not evenly distributed globally. Efforts to improve CF care in LICs, such as increasing awareness, ensuring access to therapies, and establishing specialized clinics, are essential to bridging this gap.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".