Association between Functional Small Airway Disease and FEV1 Decline in Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
RATIONALE: The small conducting airways are the major site of airflow obstruction in chronic obstructive pulmonary disease and may precede emphysema development. OBJECTIVES: We hypothesized a novel computed tomography (CT) biomarker of small airway disease predicts FEV1 decline. METHODS: We analyzed 1,508 current and former smokers from COPDGene with linear regression to assess predictors of change in FEV1 (ml/yr) over 5 years. Separate models for subjects without and with airflow obstruction were generated using baseline clinical and physiologic predictors in addition to two novel CT metrics created by parametric response mapping (PRM), a technique pairing inspiratory and expiratory CT images to define emphysema (PRM(emph)) and functional small airways disease (PRM(fSAD)), a measure of nonemphysematous air trapping. MEASUREMENTS AND MAIN RESULTS: Mean (SD) rate of FEV1 decline in ml/yr for GOLD (Global Initiative for Chronic Obstructive Lung Disease) 0-4 was as follows: 41.8 (47.7), 53.8 (57.1), 45.6 (61.1), 31.6 (43.6), and 5.1 (35.8), respectively (trend test for grades 1-4; P < 0.001). In multivariable linear regression, for participants without airflow obstruction, PRM(fSAD) but not PRM(emph) was associated with FEV1 decline (P < 0.001). In GOLD 1-4 participants, both PRM(fSAD) and PRM(emph) were associated with FEV1 decline (P < 0.001 and P = 0.001, respectively). Based on the model, the proportional contribution of the two CT metrics to FEV1 decline, relative to each other, was 87% versus 13% and 68% versus 32% for PRM(fSAD) and PRM(emph) in GOLD 1/2 and 3/4, respectively. CONCLUSIONS: CT-assessed functional small airway disease and emphysema are associated with FEV1 decline, but the association with functional small airway disease has greatest importance in mild-to-moderate stage chronic obstructive pulmonary disease where the rate of FEV1 decline is the greatest. Clinical trial registered with www.clinicaltrials.gov (NCT 00608764).
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".