Brief Report: Evaluating Early Stage Lung Cancer Survival Patterns in Patients at the Upper Age Limit for Lung Cancer Screening
Bibliographic record
Abstract
INTRODUCTION: Older individuals have an elevated lung cancer risk but may also have substantial comorbidities that preclude curative treatment options and limit the survival benefits of screening. The objective of this study was to assess early stage lung cancer survival patterns among those at the upper age limit for screening and identify older individuals who have the potential to benefit from lung cancer screening. METHODS: We identified all early stage (I or II) lung cancers diagnosed in Alberta, Canada between 2010 and 2020. Overall survival (OS) was based on the time from the date of lung cancer diagnosis to the date of death (from any cause) or censoring. We estimated OS using the Kaplan-Meier method. We present OS with 95% confidence intervals (CIs) for each age group and sex and stratified by presence of comorbidities (Charlson Comorbidity Index) and receipt of surgery. RESULTS: There were 6401 early stage lung cancers (71% stage I, 29% stage II), of which 43% and 57% were among males and females, respectively. For females, the 5-year OS was 54.7% (95% CI: 50.6-58.8), 47.2% (95% CI: 42.7-51.7), and 33.7% (95% CI: 28.4-38.9) for ages 70 to 74 years, 75 to 79 years, and 80 to 84 years, respectively. For males, the 5-year OS was 47.7% (95% CI: 43.1-52.3), 38.0% (95% CI: 33.2-42.8), and 24.2% (95% CI: 19.2-29.3) for ages 70 to 74 years, 75 to 79 years, and 80 to 84 years, respectively. Across all age groups, the 5-year OS was higher for those with fewer comorbidities and for those who received surgery as part of their treatment strategy, usually surpassing that in younger cohorts with more comorbidities or those who did not receive surgical treatment. CONCLUSIONS: Age limits for lung cancer screening should consider comorbidity and fitness for curative treatment because these can significantly influence the survival after diagnosis and treatment of early lung cancer.
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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) | 0.003 | 0.001 |
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".