Anti-estrogen Use, Estrogen Receptor Expression, Smoking Patterns, and Survival of Women with Non-Small-Cell Lung Cancer: A Manitoba Perspective
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer death worldwide. Gender differences in lung cancer outcomes are known. When compared to men, women have significantly better survival and women are more likely to develop lung cancer when nonsmokers. Research suggests estrogen plays a key role in the risk of development and outcomes of lung cancer. Accordingly, anti-estrogen use should also influence survival in female non-small cell lung cancer (NSCLC) patients. In this study we compared mortality among anti-estrogen users and non-users. METHODS: This population-based study had a retrospective study design. Using the Manitoba Cancer Registry (MCR) we identified all women diagnosed with NSCLC from 2000-2007. The Drug Program Information Network (DPIN) was accessed to establish patients that received anti-estrogens. Demographic data (e.g. smoking patterns, stage, histology) was gathered by chart review. Mortality rates for anti-estrogen users and non-users were compared using Kaplan-Meier survival functions and Cox regression models. RESULTS: 2320 women fit our patient criteria, of which 156 had received prior anti-estrogens. A positive smoking history was documented in 88%, 62% being former vs. 26% current smokers. A history of 30+ packyears was seen in 55%. Exposure to anti-estrogen was associated with a significantly decreased mortality (HR 0.718, p = 0.0031). Overall survival with anti-estrogen vs. none resulted in median survival of 1.89 vs. 0.93 years, respectively (p < 0.0001). CONCLUSIONS: Our results demonstrate that anti-estrogens are associated with decreased mortality from NSCLC. These findings supplement and reinforce past evidence that estrogen plays a key factor in the biology and outcomes of NSCLC.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".