Association between impaired diffusion capacity and small airway dysfunction: a cross-sectional study
Bibliographic record
Abstract
Background Small airway dysfunction (SAD) and impaired diffusion capacity of the lungs for carbon monoxide (DLCO) are positively associated with a worse prognosis. Individuals with both dysfunctions have been identified in clinical practice and it is unknown whether they have worse health status or need management. We conducted this study to explore the association between SAD and impairedDLCO, and the difference between the groups with two dysfunctions, with either one dysfunction and with no dysfunction. Methods This study involved subjects partly from those who had returned for the third-year follow-up (up to December 2022) of the Early Chronic Obstructive Pulmonary Disease study and those who newly participated. We assessed diffusion capacity, questionnaire, exacerbations, spirometry, impulse oscillometry (IOS) and computed tomography (CT). ImpairedDLCOwas defined asDLCO<80% predicted. Spirometry-defined SAD was defined using the percent predicted values of maximal mid-expiratory flow, and forced expiratory flow at 50% and 75% of forced vital capacity, at least two of these three values being <65% predicted after the use of a bronchodilator. IOS-defined SAD was defined when the difference in resistance at 5 and 20 Hz was >0.07 kPa·L−1·s. CT-defined SAD was defined when the percentage of expiratory low-attenuation areas <−856 HU comprised ≥15% of the total lung volume. Covariate analyses and logistic regression were performed to assess the association between impairedDLCOand SAD. Results This study involved 581 subjects. The occurrence of both spirometry- and CT-defined SAD was significantly higher in subjects with impairedDLCOthan normalDLCO. Subjects with two dysfunctions were associated with worse preceding year's exacerbations than controls. Conclusions Impaired diffusion capacity is positively associated with SAD. Subjects with impaired diffusion capacity and SAD may have a worse health status and need additional management.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".