Evaluation of annual FEV1 decline and factors associated with its accelerated deterioration in a cohort of adults with cystic fibrosis
Bibliographic record
Abstract
Introduction FEV1 is the main parameter used to quantify disease severity and activity in patients with cystic fibrosis (CF), but there is still a paucity of data regarding the determinants of annual FEV1 decline in adults. We studied our own adult CF patient cohort to quantify their mean annual FEV1 decline and identify its predictive factors. Methods Data from CF patients followed at our clinic between January 2009 and January 2012 was retrospectively analysed. Patients had to be over 18 years old and have at least 5 valid FEV1 values done during the study period, all done in a stable clinical state. Mean annual decline in FEV1 and factors associated with faster FEV1 decline were evaluated through correlation analysis and linear and multivariate regression. Results 234 patients (118 males, mean age 29) were included. 48% of patients had homozygote DF508 mutation. Pancreatic failure was noted in 85% (exocrine) and 43% (endocrine) of patients. 85% of patients were colonized with Pseudomonas sp. Mean annual FEV1 decline was 61 mL/yr (-1.5% predicted/yr). Factors associated with a faster decline in FEV1 were exocrine pancreatic failure, colonization with 3 or more bacterial species and the number of hospitalizations. Sex, age, genotype, birth cohort, baseline FEV1, use of inhaled antibiotics and home-based antibiotics treatments did not correlate with a faster decline in FEV1. Conclusion In our cohort of adult CF patients, exocrine pancreatic failure, the number of different bacterial species cultured from sputum and number of annual hospitalizations were associated with a more rapid decline in lung function, but not out-of-hospital treated exacerbations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".