Modulator Therapy in Cystic Fibrosis Patients with cis Variants in F508del Complex Allele: A Short-Term Observational Case Series
Bibliographic record
Abstract
Previous studies reported the influence of cis variants in F508del cystic fibrosis (CF) patients in their responses to CFTR modulators. The current study is a prospective, observational study involving three patients with CF and pancreatic insufficiency, carrying a complex allele including F508del with A238V, I1027T, or L467F. We report clinical data before and after 4 weeks of treatment with tezacaftor (TEZ)/ivacaftor (IVA), elexacaftor (ELX)/TEZ/IVA, and lumacaftor (LUM)/IVA for patients with complex alleles A238V, I1027T, and L467F, respectively. The 50-year-old patient bearing F508del;A238V/D1152H showed a normal sweat test (13 mEq/L) and improvements in forced expiratory volume in the first second (FEV1) (+7 points), body mass index (BMI) (+0.85), and respiratory CF Questionnaire-Revised (CFQ-R) domain (+22.2 points). The 12-year-old patient bearing F508del;I1027T/R709X showed an improvement in a sweat test (−40 mEq/l), FEV1 (+9 points) and the respiratory CFQ-R domain (+16.7 points). No changes in outcomes were observed for the 6-year-old patient F508del;L467F/F508del. Our data highlight that the reported variants do not modify the phenotypic expression of F508del. Searching L467F is crucial in CF patients with F508del nonresponsive to ELX/TEZ/IVA. Further data are needed to evaluate the clinical effect of these variants after a longer follow up.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".