Comparison of pre‐ and post‐bronchodilator lung function as predictors of mortality: The HUNT Study
Bibliographic record
Abstract
ABSTRACT Background and objective Post‐bronchodilator (BD) lung function is recommended for the diagnosis of chronic obstructive pulmonary disease (COPD). However, often only pre‐BD lung function is used in clinical practice or epidemiological studies. We aimed to compare the discrimination ability of pre‐BD and post‐BD lung function to predict all‐cause mortality. Methods Participants aged ≥40 years with airflow limitation ( n = 2538) and COPD ( n = 1262) in the second survey of the Nord‐Trøndelag Health Study (HUNT2, 1995–1997) were followed up until 31 December 2015. Survival analysis and time‐dependent area under the receiver operating characteristic curves (AUC) were used to compare the discrimination ability of pre‐BD and post‐BD lung function (percent‐predicted forced expiratory volume in the first second (FEV 1 ) (ppFEV 1 ), FEV 1 z‐score, FEV 1 quotient (FEV 1 Q), modified Global Initiative for Chronic Obstructive Lung Disease (GOLD) categories or GOLD grades). Results Among 2538 participants, 1387 died. The AUC for pre‐BD and post‐BD ppFEV 1 to predict mortality were 60.8 and 61.8 ( P = 0.005), respectively, at 20 years' follow‐up. The corresponding AUC for FEV 1 z‐score were 58.5 and 60.4 ( P < 0.001), for FEV 1 Q were 68.7 and 70.1 ( P = 0.002) and for modified GOLD categories were 62.3 and 64.5 ( P < 0.001). Among participants with COPD, the AUC for pre‐BD and post‐BD ppFEV 1 were 57.0 and 58.8 ( P < 0.001), respectively. The corresponding AUC for FEV 1 z‐score were 53.1 and 55.8 ( P < 0.001), for FEV 1 Q were 63.6 and 65.1 ( P = 0.037) and for GOLD grades were 56.0 and 57.0 ( P = 0.268). Conclusion Mortality was better predicted by post‐BD than by pre‐BD lung function; however, they differed only by a small margin. The discrimination ability using GOLD grades among COPD participants was similar.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".