Impact of elexacaftor/tezacaftor/ivacaftor on biomarkers of cystic fibrosis hepatobiliary involvement in the PUSH study
Bibliographic record
Abstract
Abstract Objectives The impact of elexacaftor/tezacaftor/ivacaftor (ETI) on cystic fibrosis (CF) hepatobiliary involvement (CFHBI) is uncertain. The goal of this study was to investigate the changes in key liver parameters in PUSH study participants who started ETI compared to those who did not (noETI). Methods PUSH was a prospective observational study of persons with CF (pwCF) (3–12 yo at PUSH entry). Linear mixed effect model (LMEM) with one knot tested if ETI changed the slope of trajectories of clinical parameters by comparing ETI to noETI. Index time (IT) for ETI was ETI start and time of ETI availability for noETI. Time zero in the LMEM was index time. Results One hundred forty‐seven participants (104 ETI, 43 noETI). At IT, the groups were similar, for age, and liver parameters. Mean duration of ETI use was 22 months. The annual rate of change over time after IT in ETI compared to noETI was significantly improved for FEV 1 % pre (+3.4/yr p = 0.02) and wt z score (+0.06/yr, p = 0.006). There was improvement for ETI vs noETI for GGT (−15%/yr, p = 0.01) and ALT (−12%/yr, p = 0.02). Participants with CFHBI on ETI demonstrated similar trends and also had improvements in GGT and GGT to platelet ratio (GPR), but there were no differences in liver stiffness or US classification changes. Conclusions GGT and GPR improve in pwCF and CFHBI who received ETI compared to noETI. There were no changes in other liver parameters. This suggests an early signal for positive impact on CFHBI, but no early improvement in fibrosis.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".