Using a Simulation Model to Assess the Impact of a Lung Cancer Screening Regimen on Wait Times and Cancer Stage Distribution
Bibliographic record
Abstract
Lung cancer is the number one cause of cancer related deaths in Ontario and throughout Canada. The 5-year survival rate for those diagnosed with lung cancer in 2020 was approximately 22.2%. Poor screening techniques is the main cause of low survival rates and late detection. Recent advancements in screening for lung cancer have led researchers to look at the benefits of using low-dose CT (LDCT) scanning to screen patients at high risk for lung cancer in order to detect the cancer in its earlier stages. There is strong evidence that using this new method of testing in lung cancer screening can reduce lung cancer related mortality by increasing the chance that the disease is detected in an earlier stage and in turn improving the patient’s chance at life saving treatment. Lung cancer screening requires LDCT resources and, based on the current recommendations, there is a concern that the new demand for imaging may exceed existing capacity of the imaging centers. This research evaluates impact of the Lung Cancer Screening Pilot for People at High Risk on the imaging resources and aims to answer the question: What would be the system performance for different imaging policies assuming a fixed imaging capacity? Administrative data from the Ottawa Hospital (TOH) as well as data from other research projects were used in order to develop and populate a simulation model. The policies that were assessed include: using biannual screening for patients who receive a negative baseline scan, using annual screening for patients with a negative baseline scan with all suspicious patients returning for a follow-up scan in six months, using annual screening for patients with a negative baseline scan with all suspicious patients returning for a follow-up scan in three months, using biannual screening for patients with a negative baseline scan with all suspicious patients returning for a follow-up scan in six months and using biannual screening for patients with a negative baseline scan with all suspicious patients returning for a follow-up scan in three months. These policies were assessed by looking at wait times for patients to be screened. Possible shift between lung cancer stages was also considered. The impact of this study is to look at system performances for different screening policies that could be used assuming a fixed imaging capacity. It represents a first step for further research should the data that is needed become available.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".