Impact of COVID-19 infection on lung function and nutritional status amongst individuals with cystic fibrosis: A global cohort study
Bibliographic record
Abstract
Background Factors associated with severe COVID-19 infection have been identified; however, the impact of infection on longer-term outcomes is unclear. The objective of this study was to examine the impact of COVID-19 infection on the trajectory of lung function and nutritional status in people with cystic fibrosis (pwCF). Methods This is a retrospective global cohort study of pwCF who had confirmed COVID-19 infection diagnosed between January 1, 2020 and December 31, 2021. Forced expiratory volume in one second percent predicted (ppFEV 1 ) and body mass index (BMI) twelve months prior to and following a diagnosis of COVID-19 were recorded. Change in mean ppFEV 1 and BMI were compared using a t -test. A linear mixed-effects model was used to estimate change over time and to compare the rate of change before and after infection. Results A total of 6,500 cases of COVID-19 in pwCF from 33 countries were included for analysis. The mean difference in ppFEV 1 pre- and post-infection was 1.4 %, (95 % CI 1.1, 1.7). In those not on modulators, the difference in rate of change pre- and post-infection was 1.34 %, (95 % CI -0.88, 3.56) per year ( p = 0.24) and -0.74 % (-1.89, 0.41) per year ( p = 0.21) for those on elexacaftor/tezacaftor/ivacaftor. No clinically significant change was noted in BMI or BMI percentile before and after COVID-19 infection. Conclusions No clinically meaningful impact on lung function and BMI trajectory in the year following infection with COVID-19 was identified. This work highlights the ability of the global CF community to unify and address critical issues facing pwCF.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".