Factors Associated with Human Small Aggressive Non–Small Cell Lung Cancer
Bibliographic record
Abstract
BACKGROUND: Some non-small cell lung cancers (NSCLC) progress to distant lymph nodes or metastasize while relatively small. Such small aggressive NSCLCs (SA-NSCLC) are no longer resectable with curative intent, carry a grave prognosis, and may involve unique biological pathways. This is a study of factors associated with SA-NSCLC. METHODS: A nested case-case study was embedded in the National Cancer Institute's Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. SA-NSCLC cases had stage T1, N3, and/or M1 NSCLC (n = 48) and non-SA-NSCLC cases had T2 to T3, N0 to N2, and M0 NSCLC (n = 329). Associations were assessed by multiple logistic regression. RESULTS: SA-NSCLCs were associated with younger age at diagnosis [odds ratio (OR)(>or=65 versus <65), 0.44; 95% confidence interval (95% CI), 0.22-0.88], female gender, family history of lung cancer, and the interaction gender*family history of lung cancer and were inversely associated with ibuprofen use (OR(yes versus no), 0.29; 95% CI, 0.11-0.76). The ORs for associating gender (women versus men) with SA-NSCLC in those with and without a family history of lung cancer were 11.76 (95% CI, 2.00-69.22) and 1.86 (95% CI, 0.88-3.96), respectively. These associations held adjusted for histology and time from screening to diagnosis and when alternative controls were assessed. CONCLUSION: SA-NSCLC was associated with female gender, especially in those with a family history of lung cancer. If these exploratory findings, which are subject to bias, are validated as causal, elucidation of the genetic and female factors involved may improve understanding of cancer progression and lead to preventions and therapies. Ibuprofen may inhibit lung cancer 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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".