Estimation of lung age via a spline method and its application in chronic respiratory diseases
Bibliographic record
Abstract
Abstract Lung age is a simplified concept that makes spirometry data easier to understand, but it is not widely used due to limitations in estimation methods. The aim of this study was to develop new equations to estimate lung age and to explore the application value of lung age in chronic respiratory diseases. Retrospective spirometric data of 18- to 80-year-old healthy subjects were used to develop the lung age estimation equations. Models were respectively built by multiple linear regression, piecewise linear regression, and the natural cubic spline method. Patients with chronic obstructive pulmonary disease (COPD) and asthma were subdivided into stages I–IV according to the severity of airflow limitation under the recommendation of the Global Initiative for Chronic Obstructive Lung Disease. Propensity score matching was performed to balance age, height and sex between healthy subjects and patients. The difference between lung age and chronological age (∆ lung age) of patients with COPD and asthma was analyzed. A total of 3409 healthy subjects, 280 patients with COPD and 285 patients with asthma data were included in the analysis. The lung age estimation equation with the best goodness of fit was built by the spline method and composed of FEV1, FEF50%, FEF75% and height as explanatory variables. ∆ lung age progressively increased with the degree of airflow limitation in patients with COPD or asthma. Lung age estimation equations were developed by a spline modeling method. Lung age may be used in the assessment of chronic respiratory patients.
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 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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".