A novel approach to differentiate obstructive lung disease from health.
Bibliographic record
Abstract
Background: Current approaches to identify obstructive lung disease (i.e., lower limit of normal, fixed ratio) may misclassify individuals. Objective: To differentiate obstructive phenotypes from health using a combination of physiological and imaging data. Methods: A topological data analysis was used to identify groups with similar features based on physiological data and extracted imaging data within a subset of participants with a FEV1/FVC ratio between 0.55 and 0.85 (zone of uncertainty) from the Canadian Cohort Obstructive Lung Disease (CanCOLD). Results: Two phenotypes (Figure a) were identified. One group “Obstructive” had worse lung function (lower FEV1/FVC, FEV1 and DLCO) and had higher total airway count, lower wall area percent. They were also more likely to have chronic cough (17% vs. 10%), higher pack years of smoking (18 vs. 10 years), and had worse survival compared with the “healthy” group (HR 2.18; 95% CI 1.14; 4.16). Conclusions: Physiological and structural data can be used to differentiate obstructive phenotypes within the zone of uncertainty. erj;66/suppl_69/PA5195/F1 F1 F1 erj;66/suppl_69/PA5195/F2 F2 F2 Figure a) Visualization of the high dimentional data, b) Radar plot of included variables
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".