Effect of Impaired Lung Function on the Development and Progression of Endobronchial Premalignant Lesions
Bibliographic record
Abstract
Background: Chronic obstructive pulmonary disease (COPD) and presence of endobronchial premalignant lesions (EPL) are individual risk factors for lung cancer (LC). However, effect of impaired lung function (ILF) on the natural history of EPL has not been explored. Patients and Methods: This study included 217 high-risk participants from a hospital-based LC surveillance cohort who underwent pulmonary function testing followed by bronchoscopy with endobronchial biopsies. Baseline histopathology diagnoses included 91 cases (41.9%) with squamous metaplasia (SM), 25 (11.5%) with squamous dysplasia (SD), 1 (0.5%) with in-situ carcinoma and 5 (2.3%) with invasive LC. Follow-up biopsies were obtained for 69 patients, and 16 (23.2%) patients demonstrated progression to a higher grade lesion. Regression models were used to evaluate the relationship between ILF and EPL. All the models were adjusted for age, gender and tobacco smoking. Results: Patients with FEV1% of <50% had 4.5 times greater risk of being diagnosed with an EPL [95% confidence interval: 1.93-10.80] and 8-fold greater risk of SD, compared to patients with FEV1% ≥80. COPD was associated with 2.7 and 4.8 times greater risk of SM and SD, respectively. The mean time to progression to a higher-grade lesion was shorter in COPD patients compared to patients without COPD (27 versus 50 months, p = 0.02). Conclusion: Our results indicate that ILF may be a predictor of prevalence and progression of EPLs among patients at high risk of LC. Therefore, spirometry can be a complementary pre-screening tool for identifying patients with EPL who need more intense LC surveillance.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".