Clinical Effectiveness of Elexacaftor/Tezacaftor/Ivacaftor in People with Cystic Fibrosis: A Clinical Trial
Bibliographic record
Abstract
Abstract Rationale The cystic fibrosis (CF) modulator drug, elexacaftor/tezacaftor/ivacaftor (ETI), proved highly effective in controlled clinical trials for individuals with at least one F508del allele, which occurs in at least 85% of people with CF. Objectives PROMISE is a postapproval study to understand the broad effects of ETI through 30 months’ clinical use in a more diverse U.S. patient population with planned analyses after 6 months. Methods Prospective, observational study in 487 people with CF age 12 years or older with at least one F508del allele starting ETI for the first time. Assessments occurred before and 1, 3, and 6 months into ETI therapy. Outcomes included change in percent predicted FEV1 (ppFEV1), sweat chloride concentration, body mass index (BMI), and self-reported respiratory symptoms. Measurements and Main Results Average age was 25.1 years, and 44.1% entered the study using tezacaftor/ivacaftor or lumacaftor/ivacaftor, whereas 6.7% were using ivacaftor, consistent with F508del homozygosity and G551D allele, respectively. At 6 months into ETI therapy, ppFEV1 improved 9.76 percentage points (95% confidence interval [CI], 8.76 to 10.76) from baseline, cystic fibrosis questionnaire–revised respiratory domain score improved 20.4 points (95% CI, 18.3 to 22.5), and sweat chloride decreased −41.7 mmol/L (95% CI, −43.8 to −39.6). BMI also significantly increased. Changes were larger in those naive to modulators but substantial in all groups, including those treated with ivacaftor at baseline. Conclusions ETI by clinical prescription provided large improvements in lung function, respiratory symptoms, and BMI in a diverse population naive to modulator drug therapy, using existing two-drug combinations, or using ivacaftor alone. Each group also experienced significant reductions in sweat chloride concentration, which correlated with improved ppFEV1 in the overall study population. Clinical trial registered with www.clinicaltrials.gov (NCT NCT04038047).
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".