A 3-Week Inpatient Rehabilitation Programme Improves Body Composition in People with Cystic Fibrosis with and Without Elexacaftor/Tezacaftor/Ivacaftor Therapy
Bibliographic record
Abstract
Background: The introduction of cystic fibrosis transmembrane conductance regulator modulators, especially the triple therapy elexacaftor, tezacaftor, ivacaftor (ETI), has improved outcomes in people with cystic fibrosis (pwCF), reducing underweight but increasing overweight rates. Objectives: This study investigates the effect of ETI on appetite control, body composition, and energy balance during a 3-week inpatient rehabilitation programme with regular exercise. Methods: In 54 pwCF (38 on ETI, 16 without ETI), changes in body composition (fat mass index, FMI; fat-free mass index, FFMI) and energy balance (calculated from body composition changes) were assessed. Appetite control was evaluated via plasma peptide YY (PYY) levels and post-exercise meal energy intake. Results: The programme significantly increased BMI (+0.3 ± 0.1 kg/m2; CI 0.1–0.4) and energy balance (+4317 ± 1976 kcal/3 weeks), primarily through FFMI gains (+0.3 ± 0.1 kg/m2; CI 0.1–0.4). Despite higher post-exercise meal energy intake and a tendency towards lower PYY levels in the ETI group, changes in body composition and energy balance did not differ between groups. This is explained by a higher prevalence of exocrine pancreatic insufficiency in the ETI group (92% vs. 50%, p < 0.001). Small sample sizes limit the interpretation of data on appetite control and energy intake. Conclusions: A 3-week inpatient rehabilitation programme improved body composition in pwCF, without resulting in a more positive energy balance with ETI therapy. This is due to a higher prevalence of pancreatic insufficiency in this group.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".