A Coordinated Approach to Lung Cancer Screening in Canada
Bibliographic record
Abstract
Background and context: Lung cancer screening with low-dose computed tomography is recommended by the Canadian Task Force on Preventive Health Care for individuals at high risk. While no organized lung cancer screening programs currently exist, several Canadian jurisdictions have begun to plan for program implementation with pilot programs, studies, or business cases. Aim: The Canadian Partnership Against Cancer (the Partnership) has supported lung cancer screening activities by initiating a series of projects to promote lung health in Canada. Strategy/Tactics: The Partnership responded to emerging evidence on lung cancer screening with the establishment of the Pan-Canadian Lung Cancer Screening Network (PLCSN) in 2012. The PLCSN brings together key stakeholders from across Canada to promote pan-Canadian collaboration and serves as a national platform for knowledge exchange. Program/Policy process: One of the first priorities of the PLCSN was the development of a consensus statement-based Lung Cancer Screening Framework for Canada in 2014. The Framework outlines key considerations for lung cancer screening programs, including screening eligibility, radiologic testing, pathology quality and reporting, diagnostic treatment and follow-up, and the inclusion of smoking cessation interventions. As the development of the Framework drew to completion, the second priority of the PLCSN was the development of national quality indicators for lung cancer screening. An initial set of ten national-level lung cancer screening quality indicators was developed for national reporting. Most recently, the PLCSN developed a list of five quality-related lung cancer screening questions that should be explored in advance of the widespread implementation of lung cancer screening programs. These considerations included eligibility, enrollment, smoking cessation, nodule management and the effect of lung cancer screening programs on projected lung cancer mortality. Other Partnership initiatives to promote lung health include health economic modeling for lung cancer screening and collecting data on evidence-based smoking cessation programs. Outcomes: These initiatives have aligned pan-Canadian lung cancer screening efforts to facilitate knowledge sharing and resource efficiency, standardization of data collection and reporting, and acceleration of lung cancer screening in Canada. As of January 2018, four provinces have completed business cases, one province has implemented a pilot study, and three trials are ongoing across the country. Partnership initiatives and resources were used by several jurisdictions to inform the development of lung health activities. What was learned: By initiating these activities in advance of organized lung cancer screening programs, the Partnership has contributed to the evidence base on best practices in lung cancer screening that will be necessary for successful program implementation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".