Changes in Forced Expiratory Volume in 1 Second over Time in COPD
Bibliographic record
Abstract
BACKGROUND: A key feature of chronic obstructive pulmonary disease (COPD) is an accelerated rate of decline in forced expiratory volume in 1 second (FEV(1)), but data on the variability and determinants of this change in patients who have established disease are scarce. METHODS: We analyzed the changes in FEV(1) after administration of a bronchodilator over a 3-year period in 2163 patients. A random-coefficient model was used to evaluate possible predictors of both FEV(1) levels and their changes over time. RESULTS: The mean (±SE) rate of change in FEV(1) was a decline of 33±2 ml per year, with significant variation among the patients studied. The between-patient standard deviation for the rate of decline was 59 ml per year. Over the 3-year study period, 38% of patients had an estimated decline in FEV(1) of more than 40 ml per year, 31% had a decline of 21 to 40 ml per year, 23% had a change in FEV(1) that ranged from a decrease of 20 ml per year to an increase of 20 ml per year, and 8% had an increase of more than 20 ml per year. The mean rate of decline in FEV(1) was 21±4 ml per year greater in current smokers than in current nonsmokers, 13±4 ml per year greater in patients with emphysema than in those without emphysema, and 17±4 ml per year greater in patients with bronchodilator reversibility than in those without reversibility. CONCLUSIONS: The rate of change in FEV(1) among patients with COPD is highly variable, with increased rates of decline among current smokers, patients with bronchodilator reversibility, and patients with emphysema.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".