Association of Dysanapsis with FEV1 Decline in Early COPD: The BEACON Cohort
Bibliographic record
Abstract
Background: We seek to assess airway dysanapsis, a known risk factor for COPD development, in a young cohort of individuals with a smoking history. Methods: Participants of the British Early COPD Network (BEACON) cohort (smokers, aged 30-45 yrs, with >10 pack-yrs tobacco history and normal (>80% predicted) FEV1) ( NCT03480347 ) were investigated with Quantitative CT. Dysanapsis (airway-to-lung ratio) was assessed as the geometric mean of airway lumen diameters in centimeters measured at 19 standard anatomic locations divided by the cube-root of total lung volume (cubic centimeters). Lower values indicate smaller airway tree caliber relative to lung size and higher values indicate larger airway tree caliber relative to lung size. Results: The annualised FEV1 decline rate in the BEACON cohort was -36.4ml/yr (95% CI (−44.4 to -28.4; P <0.001) during this study (median 34 months). Participants within quartile 1 containing the smallest airway-to-lung ratios had a faster FEV1 decline (−57 mL/y [14 mL/y]) than those in the upper quartile of highest airway-to-lung ratios (−27 mL/y [8 mL/y]). The lowest quartile also contained subjects with more emphysema (0.087% vs 0.040%, p<0.001), lower baseline FEV1 and spirometric ratios despite well balanced demographics (table). Conclusion: Contrary to prarticipants with established COPD and smaller airway-to-lung ratio had faster decline in lung function with poorer spirometry and structural measurements.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".