Lung Cancer Risk and Life-Expectancy-Based Models for Lung Cancer Screening Selection
Bibliographic record
Abstract
Lung cancer is the leading cause of death globally. There have been subsequently several lung cancer screening trials that have shown early detection can significantly improve lung cancer outcomes. However, screening everyone would be costly and unnecessary for certain individuals. Lung cancer screening through low dose computed tomography scans also exposes people to radiation and can lead to high rates of false positives. Lung cancer risk- models can reduce the number needed to screen, improve cancer detection rates and offer a more targeted screening approach. However, these models may preferentially select individuals who are not likely to benefit from screening, such as those who are older and have more comorbidities. A US life expectancy model was recently created to maximize screening efficiency by predicting the number of life years to be gained if they were screened for lung cancer.In my presentation, I will briefly discuss the economic evidence for lung cancer screening. I will explain my research project that applied the life expectancy model and a risk model to a Canadian sample. I will present the results of my analysis of the selected subgroup of individuals with a low life expectancy and low risk for lung cancer. An analysis of this subgroup’s demographics, short term outcomes, as well as lung cancer incidence was also conducted. Finally, I will end with a discussion on how these results may improve the lung cancer screening programs in the future.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".