Factors Associated With Small Aggressive Non–Small Cell Lung Cancers in the National Lung Screening Trial: A Validation Study
Bibliographic record
Abstract
BACKGROUND: A small proportion of non-small cell lung cancers (NSCLCs) have been observed to spread to distant lymph nodes (N3) or metastasize (M1) or both, while the primary tumor is small (≤3 cm, T1). These small aggressive NSCLCs (SA-NSLSC) are important as they are clinically significant, may identify unique biologic pathways, and warrant aggressive follow-up and treatment. This study identifies factors associated with SA-NSCLC and attempts to validate a previous finding that women with a family history of lung cancer are at particularly elevated risk of SA-NSCLC. METHODS: This study used a case-case design within the National Cancer Institute's National Lung Screening Trial (NLST) cohort. Case patients and "control" patients were selected based on TNM staging parameters. Case patients (n = 64) had T1 NSCLCs that were N3 or M1 or both, while "control" patients (n = 206) had T2 or T3, N0 to N2, and M0 NSCLCs. Univariate and multivariable logistic regression were used to identify factors associated with SA-NSCLC. RESULTS: In bootstrap bias-corrected multivariable logistic regression models, small aggressive adenocarcinomas were associated with a positive history of emphysema (odds ratio [OR] = 5.15, 95% confidence interval [CI] = 1.63 to 23.00) and the interaction of female sex and a positive family history of lung cancer (OR = 6.55, 95% CI = 1.06 to 50.80). CONCLUSIONS: Emphysema may play a role in early lung cancer progression. Females with a family history of lung cancer are at increased risk of having small aggressive lung adenocarcinomas. These results validate previous findings and encourage research on the role of female hormones interacting with family history and genetic factors in lung carcinogenesis and progression.
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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 | 0.022 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".