Bibliographic record
Abstract
Background and Objective: Randomized clinical trials and meta-analyses have firmly established low-dose computed tomography (LDCT) screening is an effective intervention to reduce lung cancer mortality. The purpose of this review is to address the contemporary issues relevant to health professionals and policymakers regarding lung cancer screening and the optimal approach to integrating smoking cessation with screening. Methods: A narrative review was conducted based on evidence in the literature from PubMed, Cochrane Library, and Web of Science using the keywords related to lung cancer screening. Key Content and Findings: There is a risk threshold below which there is no mortality reduction benefits from LDCT screening. An accurate lung cancer risk prediction tool such as the PLCOm2012 is more cost-effective and has significantly higher sensitivity and positive predictive value for identifying individuals who will be diagnosed with lung cancer compares to age and pack-years criteria. Screening of light and never smokers in the general population who do not currently qualify for LDCT screening will require development of an accurate risk assessment tool that includes other important risk factors such as cumulative ambient air pollution exposures. The use of geospatial mapping tools that take into account screening, diagnostic work-up and treatment resource capacities, disparity in access by social-economically deprived and underserved populations to guide screening program improvement need to be evaluated. An accurate, personalized screening LDCT management protocol is key to minimize potential harms from radiation exposure due to unnecessary imaging studies, and adverse events from biopsy or surgery for benign disease. Artificial intelligence (AI)/deep learning tools incorporating time-dependent changes in smoking behavior, age and CT findings are promising approaches to tailor individual screening intervals or diagnostic work-up referral. Improvement in the smoking cessation rate in a screening population who are older and nicotine dependent requires wider use of pharmacotherapy in addition to counselling. Primary care providers play an important role in providing smoking cessation pharmacotherapy and management of additional findings. Conclusions: Tremendous progress has been made in lung cancer screening. Additional studies to optimize screening eligibility criteria, overcome barriers to screening uptake, and personalized screening protocol can further improve the benefits/harms trade-offs.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".