Prevalence, Risk Factors, and Outcomes of Adult Interstitial Lung Abnormalities: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Rationale Incidental parenchymal abnormalities detected on chest computed tomography scans are termed interstitial lung abnormalities (ILAs). ILAs may represent early interstitial lung disease (ILD) and are associated with an increased risk of progressive fibrosis and mortality. The prevalence of ILAs is unknown, with heterogeneity across study populations. Objectives Estimate the pooled prevalence of ILAs in lung cancer screening, general population-based, and at-risk familial cohorts using meta-analysis; identify variables associated with ILA risk; and characterize ILA-associated mortality. Methods The study protocol was registered on PROSPERO (CRD42022373203), and Meta-analyses of Observational Studies in Epidemiology recommendations were followed. Relevant studies were searched on Embase and Medline. Study titles were screened and abstracts reviewed for full-text eligibility. Random effect models were used to pool prevalence estimates for specified subgroups and ILA-associated mortality risk. Risk of ILAs was estimated based on age, sex, and FVC. Quality assessment was conducted using an adapted Assessment Tool for Prevalence Studies. Measurements and Main Results The search identified 9,536 studies, with 22 included, comprising 88,325 participants. The pooled ILA prevalence was 7% (95% confidence interval [CI], 0.01–0.13) in lung cancer screening, 7% (95% CI, 0.04–0.10) in general population, and 26% (95% CI, 0.20–0.32) in familial cohorts. Pooled mortality risk was increased in those with ILAs (odds ratio, 3.56; 95% CI, 2.19–5.81). Older age, male sex, and lower FVC% were associated with greater odds of ILA. Conclusions Populations undergoing imaging for non-ILD indications demonstrate high ILA prevalence. Standardized reporting and follow-up of ILAs is needed, including defining those at greatest risk of progression to ILD.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.036 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".