Longitudinal Imaging-Based Clusters in Former Smokers of the COPD Cohort Associate with Clinical Characteristics: The SubPopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS)
Bibliographic record
Abstract
Purpose: Quantitative computed tomography (qCT) imaging-based cluster analysis identified clinically meaningful COPD former-smoker subgroups (clusters) based on cross-sectional data. We aimed to identify progression clusters for former smokers using longitudinal data. Patients and Methods: We selected 472 former smokers from SPIROMICS with a baseline visit and a one-year follow-up visit. A total of 150 qCT imaging-based variables, comprising 75 variables at baseline and their corresponding progression rates, were derived from the respective inspiration and expiration scans of the two visits. The COPD progression clusters identified were then associated with subject demography, clinical variables and biomarkers. Results: COPD severities at baseline increased with increasing cluster number. Cluster 1 patients were an obese subgroup with rapid progression of functional small airway disease percentage (fSAD%) and emphysema percentage (Emph%). Cluster 2 exhibited a decrease of fSAD% and Emph%, an increase of tissue fraction at total lung capacity and airway narrowing over one year. Cluster 3 showed rapid expansion of Emph% and an attenuation of fSAD%. Cluster 4 demonstrated severe emphysema and fSAD and significant structural alterations at baseline with rapid progression of fSAD% over one year. Subjects with different progression patterns in the same cross-sectional cluster were identified by longitudinal clustering. Conclusion: qCT imaging-based metrics at two visits for former smokers allow for the derivation of four statistically stable clusters associated with unique progression patterns and clinical characteristics. Use of baseline variables and their progression rates enables identification of longitudinal clusters, resulting in a refinement of cross-sectional clusters. Keywords: computed tomography, emphysema, functional small airway disease, longitudinal clustering
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".