Prognostic impacts of interstitial lung abnormalities on outcomes following resection for lung cancer
Bibliographic record
Abstract
INTRODUCTION: The clinical significance of interstitial lung abnormalities in patients with lung cancer undergoing curative resection remains largely unstudied. This study aimed to evaluate the prevalence of these findings among patients with lung cancer undergoing resection and assess their impact on postoperative complications and long-term survival. METHODS: This single-centre retrospective study included patients who underwent resection from 2008 to 2020. Patients with a history of lung cancer, previous lung resection or clinically evident interstitial lung disease before cancer detection were excluded. Preoperative chest scans were reviewed for interstitial lung abnormalities according to established criteria. Associations between these abnormalities and postoperative outcomes, as well as long-term survival, were analysed using multivariate models. RESULTS: Among 1802 patients with available preoperative scans, 114 (6.3%) had interstitial lung abnormalities, including 17 (0.9%) with a usual interstitial pneumonia-like pattern. Interstitial lung abnormalities were associated with older age, female sex and smoking history. Although their presence did not significantly increase the risk of postoperative complications or 30-day mortality, interstitial lung abnormalities were linked to higher long-term mortality (92 vs 61 deaths/1000 person-years, HR 1.47; 95% CI 1.05 to 2.05). The usual interstitial pneumonia-like patterns were significantly associated with increased long-term mortality (HR 2.84; 95% CI 1.36 to 5.91), whereas other patterns were not (HR 0.98; 95% CI 0.63 to 1.54). CONCLUSIONS: Interstitial lung abnormalities are common in patients with lung cancer undergoing curative surgery and are linked to demographic and smoking-related factors. While they do not significantly impact short-term surgical outcomes, usual interstitial pneumonia-like pattern is associated with worse long-term survival.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".