Safety and Efficacy of Elexacaftor/Tezacaftor/Ivacaftor for 24 Weeks or Longer in People with Cystic Fibrosis and One or More <i>F508del</i> Alleles: Interim Results of an Open-Label Phase 3 Clinical Trial
Bibliographic record
Abstract
Results of an Open-Label Phase 3 Clinical TrialTo the Editor:Cystic fibrosis (CF) is caused by mutations in the CFTR (CF transmembrane conductance regulator) gene (1).The most common CFTR mutation in populations of European descent is F508del, with up to 90% of people with CF (pwCF) having one or more F508del alleles (2-4).Two pivotal phase 3 studies of a triple-combination regimen consisting of small-molecule CFTR modulators elexacaftor (ELX)/tezacaftor (TEZ)/ivacaftor (IVA) showed unprecedented clinical efficacy in pwCF 12 years old or older heterozygous for the F508del-CFTR mutation and a minimal function mutation (F/MF; an MF mutation results in either no CFTR protein or a CFTR protein that does not respond to IVA and TEZ/IVA in vitro) or homozygous for F508del (F/F) (5-7).ELX/TEZ/IVA was generally safe and well tolerated in both studies (5, 6).Eligible participants from both pivotal phase 3 studies could elect to participate in an ongoing, phase 3, open-label extension (OLE) study to evaluate the long-term safety and efficacy of ELX/TEZ/IVA.We report the results of an interim analysis of the OLE performed after the last ongoing participant had completed the Week 24 visit.Final results from this study will be published after study completion.These interim results have been accepted for oral presentation (8). Methods
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".