Associations of hiatus hernia with CT-based interstitial lung changes: the MESA Lung Study
Bibliographic record
Abstract
Background Hiatus hernia (HH) is prevalent in adults with pulmonary fibrosis. We hypothesised that HH would be associated with markers of lung inflammation and fibrosis among community-dwelling adults and stronger amongMUC5B(rs35705950) risk allele carriers. Methods In the Multi-Ethnic Study of Atherosclerosis, HH was assessed from cardiac and full-lung computed tomography (CT) scans performed at Exam 1 (2000–2002, n=3342) and Exam 5 (2010–2012, n=3091), respectively. Percentage of high attenuation areas (HAAs; percentage of voxels with attenuation between −600 and −250 HU) was measured from cardiac and lung scans. Interstitial lung abnormalities (ILAs) were examined from Exam 5 scans (n=2380). Regression models were used to examine the associations of HH with HAAs, ILAs and serum matrix metalloproteinase-7 (MMP-7), and adjusted for age, sex, race/ethnicity, educational attainment, smoking, height, weight and scanner parameters for HAA analysis. Results HH detected from Exam 5 scans was associated with a mean percentage difference in HAAs of 2.23% (95% CI 0.57–3.93%) and an increase of 0.48% (95% CI 0.07–0.89%) per year, particularly inMUC5Brisk allele carriers (p-value for interaction=0.02). HH was associated with ILAs among those <80 years of age (OR for ILAs 1.78, 95% CI 1.14–2.80) and higher serum MMP-7 level among smokers (p-value for smoking interaction=0.04). Conclusions HH was associated with more HAAs over time, particularly amongMUC5Brisk allele carriers, and ILAs in younger adults, and may be a risk factor in the early stages of interstitial lung disease.
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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) | 0.001 | 0.000 |
| 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".