The Descriptive Epidemiology of Primary Lung Cancer in an Alberta Cohort with a Mutivariate Analysis of Survival to Two Years
Bibliographic record
Abstract
BACKGROUND: Lung cancer contributes significantly to cancer morbidity and mortality. Although case fatality rates have not changed significantly over the past few decades, there have been advances in the diagnosis, staging and management of lung cancer. OBJECTIVE: To describe the epidemiology of primary lung cancer in an Alberta cohort with an analysis of factors contributing to survival to two years. PATIENTS AND METHODS: Six hundred eleven Albertans diagnosed with primary lung cancer in 1998 were identified through the Alberta Cancer Registry. Through a chart review, demographic and clinical data were collected for a period of up to two years from the date of diagnosis. RESULTS: The mean age at diagnosis was 66.5 years. The majority of cases (92%) were smokers. Adenocarcinoma, followed by squamous cell carcinoma, were the most frequent nonsmall cell lung cancer histologies. Adenocarcinoma was more frequent in women, and squamous cell carcinoma was more frequent in men. The overall two- year survival rates for nonsmall cell, small cell and other lung cancers were 24%, 10% and 13%, respectively. In multivariate analysis, stage, thoracic surgery and chemotherapy were significantly associated with survival to two years in nonsmall cell carcinoma; only stage and chemotherapy were significant in small cell carcinoma. CONCLUSIONS: This study provides a Canadian epidemiological perspective, which generally concurs with the North American literature. Continued monitoring of the epidemiology of lung cancer is essential to evaluate the impact of advances in the diagnosis, staging and management of lung cancer. Further clinical and economic analysis, based on data collected on this cohort, is planned.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".