Optimal Pathways to Lung Cancer Screening in Primary Care Settings: A Scoping Review
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer-related deaths worldwide, and delayed detection contributes to poor outcomes. Primary care plays a crucial role in early diagnosis, but detecting lung cancer early remains challenging for general practitioners (GPs). Therefore, the aim of this scoping review was to identify optimal strategies and pathways for lung cancer screening (LCS) in primary care settings globally. We conducted a scoping review by searching PubMed, Scopus, and the Cochrane Library for relevant studies published in the past 10 years. Our keywords included "lung cancer", "primary care", "early detection", "screening", "best practices", and "pathways". We included randomized controlled trials, cross-sectional studies, and cohort studies focused on lung cancer screening in primary care. We extracted data on study characteristics, screening pathways, and key findings. We identified 18 studies that met our inclusion criteria. Important strategies for LCS included the use of shared decision-making tools, electronic health record (HER) prompts, risk prediction models, community outreach, and integration with smoking cessation programs. Barriers to implementation included the lack of provider familiarity with guidelines, time constraints, and patient factors. Healthcare professionals and policy makers in primary care settings can leverage this information to integrate the most effective screening strategies into their care, thus enhancing early detection rates and subsequently reducing global lung cancer morbidity and mortality.
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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 | 0.014 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".