Chest <scp>CT</scp>‐assessed comorbidities and all‐cause mortality risk in <scp>COPD</scp> patients in the <scp>BODE</scp> cohort
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: The availability of chest computed tomography (CT) imaging can help diagnose comorbidities associated with chronic obstructive pulmonary disease (COPD). Their systematic identification and relationship with all-cause mortality have not been explored. Furthermore, whether their CT-detected prevalence differs from clinical diagnosis is unknown. METHODS: The prevalence of 10 CT-assessed comorbidities was retrospectively determined at baseline in 379 patients (71% men) with mild to severe COPD attending pulmonary clinics. Anthropometrics, smoking history, dyspnoea, lung function, exercise capacity, BODE (BMI, Obstruction, Dyspnoea and Exercise capacity) index and exacerbations rate were recorded. The prevalence of CT-determined comorbidities was compared with that recorded clinically. Over a median of 78 months of observation, the independent association with all-cause mortality was analysed. A 'CT-comorbidome' graphically expressed the strength of their association with mortality risk. RESULTS: Coronary artery calcification, emphysema and bronchiectasis were the most prevalent comorbidities (79.8%, 62.7% and 33.9%, respectively). All were underdiagnosed before CT. Coronary artery calcium (hazard ratio [HR] 2.09; 95% CI 1.03-4.26, p = 0.042), bronchiectasis (HR 2.12; 95% CI 1.05-4.26, p = 0.036) and low psoas muscle density (HR 2.61; 95% CI 1.23-5.57, p = 0.010) were independently associated with all-cause mortality and helped define the 'CT-comorbidome'. CONCLUSION: This study of COPD patients shows that systematic detection of 10 CT-diagnosed comorbidities, most of which were not detected clinically, provides information of potential use to patients and clinicians caring for them.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".