An opinion piece: Extracting multisystem insights from a single chest CT scan in patients with COPD
Bibliographic record
Abstract
Chest computed tomography (CT) imaging provides high-resolution visualization of thoracic structures and is used for radiologic assessment in patients with chronic obstructive pulmonary disease (COPD). Quantitative CT (qCT) biomarkers are objective and reproducible, and offer prognostic information, but there is limited clinical adoption. This opinion piece summarizes the key qCT biomarkers developed to describe COPD pathophysiology such as emphysema, small airway disease, airway remodeling, vascular pruning, and abnormal body composition. These measurements have been shown to be independent predictors of clinical outcomes such as lung function decline, future exacerbations, and mortality. Furthermore, advancements in artificial intelligence now make fully-automated extraction of all qCT biomarkers possible from a single inspiratory chest CT scan. Additional research is required to standardize qCT extraction and address concerns about repeatability/reproducibility and lack of clinically meaningful healthy cutoffs. Such advancements will allow for the implementation of personalized action plans in COPD patients that target specific disease-related structural abnormalities.
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".