Elexacaftor–Tezacaftor–Ivacaftor for Cystic Fibrosis with a Single Phe508del Allele
Bibliographic record
Abstract
BACKGROUND: mutation and a minimal-function mutation (Phe508del-minimal function genotype), the next-generation CFTR corrector elexacaftor, in combination with tezacaftor and ivacaftor, improved Phe508del CFTR function and clinical outcomes. METHODS: ) at week 4. RESULTS: that was 13.8 points higher at 4 weeks and 14.3 points higher through 24 weeks, a rate of pulmonary exacerbations that was 63% lower, a respiratory domain score on the Cystic Fibrosis Questionnaire-Revised (range, 0 to 100, with higher scores indicating a higher patient-reported quality of life with regard to respiratory symptoms; minimum clinically important difference, 4 points) that was 20.2 points higher, and a sweat chloride concentration that was 41.8 mmol per liter lower (P<0.001 for all comparisons). Elexacaftor-tezacaftor-ivacaftor was generally safe and had an acceptable side-effect profile. Most patients had adverse events that were mild or moderate. Adverse events leading to discontinuation of the trial regimen occurred in 1% of the patients in the elexacaftor-tezacaftor-ivacaftor group. CONCLUSIONS: Elexacaftor-tezacaftor-ivacaftor was efficacious in patients with cystic fibrosis with Phe508del-minimal function genotypes, in whom previous CFTR modulator regimens were ineffective. (Funded by Vertex Pharmaceuticals; VX17-445-102 ClinicalTrials.gov number, NCT03525444.).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".