Does carbon dioxide retention during exercise predict a more rapid decline in FEV1 in cystic fibrosis?
Bibliographic record
Abstract
BACKGROUND: Carbon dioxide (CO2) retention during exercise is uncommon in mild to moderate lung disease in cystic fibrosis (CF). The ability to deal with increased CO2 is dependent on the degree of airflow limitation and inherent CO2 sensitivity. CO2 retention (CO2R) can be defined as a rise in P(ET)CO2 tension of > or =5 mm Hg with exercise together with a failure to reduce P(ET)CO2 tension after peak work by at least 3 mm Hg by the termination of exercise. AIM: To ascertain if carbon dioxide retention during exercise is associated with more rapid decline in lung function. METHODS: Annual spirometric and exercise data from 58 children aged 11-15 years, with moderate CF lung disease between 1996 and 2002 were analysed. RESULTS: The mean FEV1 at baseline for the two groups was similar; the CO2R group (n = 15) was 62% and the non-CO2 retention group (CO2NR) was 64% (n = 43). The decline in FEV1 after 12 months was -3.2% (SD 1.1) in the CO2R group and -2.3% (SD 0.9) in the CO2NR group. The decline after 24 months was -6.3% (SD 1.3) and -1.8% (SD 1.1) respectively. After 36 months, the decline in FEV1 was -5.3% (SD 1.2) and -2.6% (SD 1.1) respectively. The overall decline in lung function was 14.8% (SD 2.1) in the CO2R group and 6.7% (SD 1.8) in the CO2NR group. Using the primary outcome measure as a decline in FEV(1) of >9%, final multivariate analysis showed that the relative risks for this model were (95% CIs in parentheses): DeltaP(ET)CO2 11.61 (3.41 to 24.12), peak VO2 1.23 (1.10 to 1.43), and initial FEV(1) 1.14 (1.02 to 1.28). CONCLUSION: Results show that the inability to defend carbon dioxide during exercise is associated with a more rapid decline in lung function.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".