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Evaluating Long-Term Effectiveness of Cystic Fibrosis Modulator Therapies after Rapid Adoption: A Dual-Approach Study

2025· article· en· W4414079915 on OpenAlexaff
Pedro Miranda Afonso, Grace C Zhou, Weiji Su, Elizabeth A. Cromwell, Christopher H. Goss, Ruth H. Keogh, Theodore G. Liou, Bruce C. Marshall, Nicole Mayer-Hamblett, Wayne J. Morgan, Joshua S. Ostrenga, David J. Pasta, Michael S. Schechter, Sanja Stanojevic, Claire Wainwright, Rhonda D. Szczesniak, Eleni‐Rosalina Andrinopoulou

Bibliographic record

VenueAnnals of the American Thoracic Society · 2025
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsDalhousie University
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesUniversity of UtahNational Institutes of HealthUK Research and InnovationCystic Fibrosis Foundation
KeywordsIvacaftorCystic fibrosisAffect (linguistics)MEDLINECystic fibrosis transmembrane conductance regulator

Abstract

fetched live from OpenAlex

RATIONALE: Modulator therapies like ivacaftor have revolutionized clinical management of cystic fibrosis, showing marked short-term benefits in trials but heterogeneous findings in long-term observational studies. Since newer modulators have become the standard of care for the majority living with cystic fibrosis in the United States, characterizing long-term effectiveness with real-world data is increasingly difficult because of the lack of contemporary comparator groups for performing between-subjects analyses. OBJECTIVES: To determine the extent to which ivacaftor preserves long-term lung function and compare the results of within- and between-subjects analyses for evaluating its real-world effectiveness. METHODS: This retrospective cohort study used data from the U.S. Cystic Fibrosis Foundation Patient Registry (2003-2016). We used two approaches to evaluate ivacaftor effectiveness on percent predicted forced expiratory volume in 1 second (ppFEV1): 1) within-subject comparisons of ppFEV1 before and after ivacaftor initiation; and 2) comparisons between ivacaftor-treated and untreated individuals with similar disease pathology. We modeled data from 560 ivacaftor-treated individuals with the G551D variant. For between-subjects comparisons, we used propensity scores to match the treated group with 2,800 untreated F508del homozygous individuals. Modulator initiation bias was assessed and accounted for in each model. RESULTS: Our results showed an initial average improvement in ppFEV1 in ivacaftor-treated children and adults (ranging from 4.54% to 6.53% predicted based on within-subject comparison of before vs. after ivacaftor initiation). There was a slower decline in adults, compared with children. These ivacaftor-treated cohorts experienced less decline relative to their F508del homozygous counterparts (between-group differences in treated vs. control ranged from 0.36% to 0.64% predicted). Both the within- and between-subjects comparisons demonstrated similar degrees of ivacaftor effectiveness. However, small differences between the two approaches were observed in younger individuals. CONCLUSIONS: Ivacaftor was associated with improved ppFEV1 across all age groups, with the magnitude of improvement roughly 50% of that observed in clinical trials. The results support the need to account for modulator initiation bias and the use of within-subject analysis in future CFTR (cystic fibrosis transmembrane conductance regulator) modulator effectiveness studies, but caution is advised in younger individuals because of developmental changes that may affect pre- and post-treatment comparability.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.041
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.216

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0410.041
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.084
GPT teacher head0.457
Teacher spread0.373 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2025
Admission routes1
Has abstractyes

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