Lumacaftor–Ivacaftor in Patients with Cystic Fibrosis Homozygous for Phe508del<i>CFTR</i>
Bibliographic record
Abstract
BACKGROUND: Combination treatment with the cystic fibrosis transmembrane conductance regulator (CFTR) modulators tezacaftor (VX-661) and ivacaftor (VX-770) was designed to target the underlying cause of disease in patients with cystic fibrosis. METHODS: through week 24 (calculated as a percentage) was a key secondary end point. RESULTS: in favor of tezacaftor-ivacaftor over placebo were 4.0 percentage points and 6.8%, respectively (P<0.001 for both comparisons). The rate of pulmonary exacerbation was 35% lower in the tezacaftor-ivacaftor group than in the placebo group (P=0.005). The incidence of adverse events was similar in the two groups. Most adverse events were of mild severity (in 41.8% of patients overall) or moderate severity (in 40.9% overall), and serious adverse events were less frequent with tezacaftor-ivacaftor (12.4%) than with placebo (18.2%). A total of 2.9% of patients discontinued the assigned regimen owing to adverse events. Fewer patients in the tezacaftor-ivacaftor group than in the placebo group had respiratory adverse events, none of which led to discontinuation. CONCLUSIONS: The combination of tezacaftor and ivacaftor was efficacious and safe in patients 12 years of age or older who had cystic fibrosis and were homozygous for the CFTR Phe508del mutation. (Funded by Vertex Pharmaceuticals; EVOLVE ClinicalTrials.gov number, NCT02347657 .).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".