Eligibility for low-dose computerized tomography screening among asbestos-exposed individuals
Bibliographic record
Abstract
OBJECTIVES: The study aimed to incorporate an estimate of risk for asbestos exposure in the Canadian Cancer Risk Management Lung Cancer (CRMM-LC) microsimulation model. METHODS: In CRMM-LC, a 3-year probability of developing lung cancer can be derived from different risk profiles. An asbestos-exposed cohort was simulated and different scenarios of low-dose computerized tomography (LDCT) screening were simulated. RESULTS: As annual LDCT screening among non-asbestos-exposed individuals is less cost-effective than biennial screening, all the scenarios modeled for an asbestos-exposed cohort were biennial. For individuals with a two-fold risk of asbestos-induced lung cancer to be eligible for biennial LDCT screening, a smoking history of ≥15 pack-years would be necessary. For non-smokers with asbestos exposure resulting in a relative risk (RR) for lung cancer, it is not cost-effective to screen those with a RR of 5, but it is cost-effective to screen those with a RR of 10 (the heavily exposed). CONCLUSION: Asbestos-exposed individuals with an estimated two-fold or more risk of lung cancer from asbestos-exposure are eligible for LDCT screening at all ages from 55-74 years if they have a cigarette smoking history of ≥15 pack-years. Asbestos-exposed individuals who are lifelong non-smokers are eligible for LDCT screening at all ages from 55-74 years if they have accumulated a degree of asbestos exposure resulting in an estimated risk of lung cancer of ≥10.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".