The variable relationship between changes in lung density and FEV1 in smokers
Bibliographic record
Abstract
Background: Computed tomographic (CT) measures of lung density are potential intermediate study endpoints for clinical studies but to do so, the clinical associations of these changes must be understood and benchmarked against accepted clinical endpoints such as change in lung function. We investigated the relationship between changes in lung density and FEV1 in smokers with and without COPD. Methods: The 15th percentile of CT lung density was obtained from the scans of 3390 smokers who completed baseline and 5-yr follow-up COPDGene study visits. The relationship between the change in lung density and change in FEV1 was assessed using multivariable mixed models. Separate models were performed in smokers at risk, with PRISM (preserved ratio and impaired spirometry) smokers, and with COPD by GOLD stage. Results: In smokers with PRISM an increase in lung density was significantly associated with a decrease in FEV1 (estimate per 1 g/L increase, -3.0 mL P=0.007). In contrast, in GOLD 3-4 COPD, there was a direct relationship between loss of lung density and loss of lung function. One g/L decrease in lung density was associated with a decrease of 4.8 mL FEV1 (P=0.0003) over the 5-yr follow-up. Conclusion: A decline in lung density was associated with an increase or decrease in FEV1 based on disease severity. This FEV1-CT lung density relationship should be taken into account when using CT imaging as an endpoint in COPD longitudinal studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".