Cardiopulmonary Exercise Testing Provides Additional Prognostic Information in Cystic Fibrosis
Bibliographic record
Abstract
RATIONALE: The prognostic value of cardiopulmonary exercise testing (CPET) for survival in cystic fibrosis (CF) in the context of current clinical management, when controlling for other known prognostic factors, is unclear. OBJECTIVES: peak) following rigorous adjustment for other predictors. METHODS: Data from 10 CF centers in Australia, Europe, and North America were collected retrospectively. A total of 510 patients completed a cycle CPET between January 2000 and December 2007, of which 433 fulfilled the criteria for a maximal effort. Time to death/lung transplantation was analyzed using Cox proportional hazards regression. In addition, phenotyping using hierarchical Ward clustering was performed to characterize high-risk subgroups. MEASUREMENTS AND MAIN RESULTS: peak in % predicted (hazard ratio [HR], 0.964; 95% confidence interval [CI], 0.944-0.986), peak work rate (% predicted; HR, 0.969; 95% CI, 0.951-0.988), ventilatory equivalent for oxygen (HR, 1.085; 95% CI, 1.041-1.132), and carbon dioxide (HR, 1.060; 95% CI, 1.007-1.115) (all P < 0.05) were significant predictors of death or lung transplantation at 10-year follow-up. Phenotyping revealed that CPET-derived measures were important for clustering. We identified a high-risk cluster characterized by poor lung function, nutritional status, and exercise capacity. CONCLUSIONS: CPET provides additional prognostic information to established predictors of death/lung transplantation in CF. High-risk patients may especially benefit from regular monitoring of exercise capacity and exercise counseling.
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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 | 0.007 |
| 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 | 0.001 | 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".