Relationship between theratyping in nasal epithelial cells and clinical outcomes in people with cystic fibrosis
Bibliographic record
Abstract
Background In people with cystic fibrosis (pwCF), human nasal epithelial cell (HNEC) cultures can be used to assess response to CF transmembrane conductance regulator (CFTR) modulators. However, thresholds of in vitro responses that predict clinical benefit remain poorly understood. In this study we describe the concordance between in vitro response in HNECs and clinical outcomes in pwCF harbouring the F508del variant, treated with either lumacaftor/ivacaftor, tezacaftor/ivacaftor or elexacaftor/tezacaftor/ivacaftor. Methods Response of HNECs to CFTR modulators was assessed by CFTR-mediated chloride current stimulated by forskolin or inhibited by CFTRinh-172 in both pwCF and healthy controls. Clinical response was defined as change in forced expiratory volume in 1 s (FEV 1 ), lung clearance index (LCI), sweat chloride or respiratory domain of the Cystic Fibrosis Questionnaire-Revised (CFQ-R) between baseline and within 3 months after the start of modulator treatment. Results In 58 unique in vitro –clinical pairs, in vitro measures of functional rescue correlated with changes in FEV 1 , LCI and sweat chloride, but not CFQ-R. The concordance between in vitro response and clinical outcomes was highest when a composite outcome was used. For example, an in vitro response of 10% of healthy controls had positive and negative predictive values of 90.5% and 100%, respectively, for a clinical response in either FEV 1 , LCI or sweat chloride. Conclusions We identified thresholds of nasal epithelial cell theratype response in pwCF to predict clinical benefit from CFTR modulator therapy. The utility of this therapy testing platform to predict a clinical response improves when multiple clinical outcome measures are combined.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".