Determinants of COPD Stage Progression and Regression: a Markov Transition Analysis of The COPDGene Cohort
Bibliographic record
Abstract
ABSTRACT Rationale Chronic obstructive pulmonary disease (COPD) is a leading cause of death but with variable progression. Objective Estimate factors influencing transition rates between PRISm and GOLD stages. Methods Using a Markov multistate model, transition rates between GOLD-0, PRISm and GOLD-1, GOLD-2 and GOLD 3-4 were estimated for 5,728 US adult ever cigarette users from the COPDGene cohort over 10-years. We calculated one and five-year transition probabilities for progressive and regressive transitions and estimated the mean sojourn time for severity states. Main Results GOLD-1 and PRISm individuals spent the least time in any single stage (GOLD-1: 6 years; PRISm: 7 years). PRISm and GOLD-1 individuals were equally likely to transition to GOLD-2 vs. GOLD-0 (PRISm: HR 1.09, 95% confidence interval [CI] 0.90-1.33, GOLD-1 (HR 1.15, 95%CI 0.93-1.42) per five-year period, but rarely transition between PRISm and GOLD-1. Individuals at GOLD-0 were equally likely to progress to GOLD-1 or PRISm (HR 1.11, 95%CI 0.93-1.31) but the transient time for this stage was the longest of any GOLD stage (16 years, 95%CI 15.2-17.3). GOLD-2 was the most likely stage to progress (HR 2.4, 95%CI 1.9-3.02) to GOLD 3-4 vs. regress to GOLD-1. For GOLD-2 individuals, current smoking status (HR 0.84, 95%CI 0.67-1.06) or intensity (HR 0.84, 95%CI 0.54-1.29) was not associated with disease progression. Conclusions GOLD-1 and PRISm are the most transient stages equally likely to regress to GOLD-0 or progress to GOLD-2 and may benefit from smoking cessation interventions. GOLD-2 individuals are the most likely to progress and may benefit most from targeted disease interventions.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".