Hormone Replacement Therapy and Outcomes for Women with Non-Small-Cell Lung Cancer: Can An Association be Confirmed?
Bibliographic record
Abstract
BACKGROUND: A recent report suggested that women who had been taking hormone replacement therapy (HRT) experienced significantly decreased survival after a lung cancer diagnosis. Given the large cohort of women who have received HRT, it is important to try to confirm that association. METHODS: We reviewed female patients diagnosed with lung cancer at our institution between January 1999 and December 2003 for age at diagnosis, disease stage, treatment, smoking history, hrt, performance status, weight loss, age at menopause, and overall survival. Patients were excluded if they had small-cell lung cancer or an unknown primary cancer, or if they had had previous or synchronous non-lung, non-skin cancers. Statistical analysis used the chi-square test for categorical variables and the Kaplan-Meier method and Cox regression model for univariate and multivariate analyses of overall survival. RESULTS: Of 397 eligible patients, most (68%) were stage iii or iv. The group included very few never-smokers (5%). The proportion of patients with experience of prior or current hrt was 29%, and no effect on overall survival was observed. Median survival was 13 months in the non-hrt group and 14 months in the hrt group. Significant factors predicting for overall survival included performance status, stage, and weight loss. CONCLUSIONS: Stage, performance status, and weight loss are the most powerful predictors of survival for women with non-small-cell lung cancer. As compared with non-hrt users, patients with prior hrt use did not have inferior outcomes, failing to duplicate previously published results.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".