184P A framework to support implementation of low-dose computed tomography (LDCT) lung cancer screening: Research methodology and opportunities for impact
Bibliographic record
Abstract
The Lung Cancer Policy Network, a global multi-stakeholder initiative of experts in lung cancer, has developed a framework to inform implementation of LDCT screening. The framework and associated online toolkit aim to support those involved in the planning and delivery of LDCT screening programmes around the world. System readiness refers to the ability of health systems to rapidly and sustainably adapt policies, processes and infrastructure to support the integration of new components of care. Assessing health system readiness is therefore an important step when planning the implementation of screening programmes. With this understanding, a bespoke framework to assess readiness for implementation of LDCT screening was developed. The framework was informed by a review of existing peer-reviewed and grey literature from 2010–22, expert interviews and insights from Network members. The framework was further refined after it was applied to five countries where screening implementation is underway: Canada, Poland, South Korea, the UK and the US. The implementation framework and online toolkit were made publicly available in March 2023. To date, the framework has been downloaded over 800 times and the toolkit has been used by almost 1,500 people. The framework will help with assessing health system readiness for screening implementation at a national or regional level. The framework is organised into six domains, each consisting of metrics to identify gaps in screening requirements (including local infrastructure, technical and workforce capacity, governance, data flows and existing care pathways), and measures to address these. Researchers and decision-makers can plan and resource their screening programmes by using this information and the supporting material in the online toolkit. To our knowledge, this is the first framework to support implementation of LDCT screening programmes globally. Application of the framework to a given health system can provide evidence to inform policy considerations for implementation, facilitating high-quality, equitable and cost-effective screening.
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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) | 0.000 | 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) | 0.001 | 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".