Respiratory Function as a Prognostic Factor for Lung Cancer in Screening and General Populations
Bibliographic record
Abstract
Abstract Rationale Despite advancements in screening, lung cancer remains the leading cause of cancer-related mortality globally. Objectives To investigate respiratory function as a prognostic factor for survival in the UK Biobank, a population-based cohort of more than 500,000 participants, and the NLST (National Lung Screening Trial), a high-risk screening population of more than 50,000 screenees. Methods Participants with an incident lung cancer diagnosis and spirometry-assessed lung function were included. Lung cancer was measured as the ratio of forced expiratory volume in 1 second (FEV1) and forced vital capacity and percentage of predicted FEV1. Multivariable Cox proportional hazards models were fitted to estimate the impact of lung function on 5-year overall survival in populations with different baseline lung cancer risks. Results A total of 2,690 and 609 patients were included in the analysis from the UK Biobank and the NLST, respectively. In the UK Biobank, a higher percentage of predicted FEV1 and ratio were associated with better survival after lung cancer diagnosis, with hazard ratios of 0.97 (95% confidence interval [CI], 0.95–1.00 per 10% increase) and 0.95 (95% CI, 0.90–1.00 per 10% increase), respectively. No statistically significant results were found when assessing the data from the NLST study. Conclusions Impaired lung function was associated with poorer survival for patients with lung cancer in the general population, although this was less clear in a high-risk, screening-eligible population. This highlights the potential clinical importance of respiratory function as a prognostic factor in lung cancer in the general population and presents a possibility for personalized cancer management.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".