CAR/CSTR Practice Guideline on CT Screening for Lung Cancer
Bibliographic record
Abstract
Lung cancer is the second-most diagnosed cancer and the leading cause of cancer-related death in Canada. The updated CAR/CSTR Practice Guideline on CT Screening for Lung Cancer reflects advancements in evidence since the 2016 guideline, including findings from the NELSON trial and preliminary data from multiple provincial lung cancer screening programs, and aims to support Canadian diagnostic imaging departments in implementing organized lung cancer screening programs. The guideline emphasizes screening with the use of low-dose CT (LDCT) to reduce lung cancer mortality in appropriately selected individuals with increased risk of lung cancer, using eligibility criteria based on risk prediction models such as the PLCO m2012 . It outlines training requirements for radiologists, standardized CT and reporting protocols, quality assurance measures, and the integration of AI tools for nodule risk stratification. The document also highlights emerging areas for investigation, including the potential for biennial screening and equitable access to programs across Canada.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".