Analysis of mucolytic therapy use in cystic fibrosis based on national registries
Bibliographic record
Abstract
An analysis of mucolytic drug use was conducted using data from national cystic fibrosis (CF) patient registries in Europe, the US, Canada, Australia, and Russia. Mucolytic therapy is a key component of CF treatment. Mucolytic drugs are used in patients of all ages. During the study period, dornase alfa was prescribed to the largest number of patients across all registries. In Russia, this drug is available to all patients, largely owing to the “14 High- Cost Nosologies” program and the presence of a dornase alfa biosimilar produced in Russia. According to registry data, dornase alfa was used more frequently in Russia than in other countries – up to 95.8% in 2019, with 25.9% of patients receiving a second dose intranasally (in 2023). In the US, dornase alfa was prescribed to up to 90% of patients, in Australia – up to 60%, in Canada – up to 52% of adult patients, and in the EU, more than 50% of patients received this drug. Among rapid-acting mucolytics, hypertonic saline inhalation was the most commonly prescribed, accounting for up to 77% of patients in the US, up to 72.7% in Russia, up to 52.3% in the EU, up to 45.4% in Australia, and up to 41.6% in Canada among adult CF patients. Mannitol inhalation was used in individual cases, primarily in adolescence (up to 15.7% of adolescents with CF in Australia in 2020). In recent years, a slight decrease in the use of mucolytic medications has been observed across all countries under study.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".