Prevalence and outcomes of interstitial lung abnormalities in a Canadian lung cancer screening trial
Bibliographic record
Abstract
RATIONALE Interstitial lung abnormalities (ILA) are radiographic interstitial findings that are incidentally identified on chest imaging performed for other reasons. ILAs have been identified on chest computed tomography (CT) performed as part of lung cancer screening programs.OBJECTIVES This study characterizes the prevalence and outcomes associated with ILA in a Canadian lung cancer screening cohort.METHODS Patients were identified from the Alberta lung cancer screening trial, a 5-year cohort study. CT reports were screened for terms associated with ILA including specific radiologic features and CT patterns. These were further characterized as non-subpleural, subpleural nonfibrotic and subpleural fibrotic, consistent with published definitions. Retrospective chart review was conducted for those with ILA to define demographics, lung function, and longitudinal outcomes including subsequent investigations for interstitial lung disease (ILD), treatment, and survival.MEASUREMENTS AND MAIN RESULTS Of 806 patients in the lung cancer screening study, 30 (3.7%) were identified as having ILA, with two-thirds (67%) having subpleural fibrotic abnormalities. Half of patients were referred to a Respirologist and underwent pulmonary function testing. Over a median follow-up period of two years, none were diagnosed with an idiopathic interstitial pneumonia, or started on immunomodulatory or antifibrotic therapy. Three of 30 (10%) patients demonstrated disease progression over time, all of whom had subpleural fibrotic ILA on baseline chest CT.CONCLUSIONS The prevalence of ILA in this Canadian lung cancer screening cohort was 3.7%. These data should inform the development of standardized reporting and follow-up for ILA as lung cancer screening programs are implemented.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".