Lung Cancer Screening With Low-Dose CT: A Systematic Review
Bibliographic record
Abstract
Lung cancer is highly prevalent worldwide and is the leading cause of cancer-related death in Portugal. There is increasing evidence that low-dose computed tomography (LDCT) screening reduces mortality; however, few countries have implemented screening strategies. This review aims to gather the best evidence to assess the relevance of implementing lung cancer screening. A search was conducted for clinical practice guidelines (CPGs), systematic reviews (SRs), and meta-analyses (MAs) published between January 1, 2010, and January 31, 2024, as well as randomized controlled trials (RCTs) published between January 1, 2019, and January 31, 2024, indexed in databases such as the National Guideline Clearinghouse, Cochrane Library, Guideline Finder, Canadian Medical Association, Evidence-Based Medicine Online, Database of Abstracts of Reviews of Effectiveness (DARE), and PubMed. The MeSH terms used were "lung cancer" and "screening". To evaluate the level of evidence (LE) and strength of recommendation (SR) in the included MAs, the Strength of Recommendation Taxonomy (SORT) from the American Academy of Family Physicians was applied. A total of 460 articles were found, with two CPGs, six MAs, two SRs, and one RCT being selected. The CPGs recommend screening with LDCT for smokers with a smoking history of more than 20 pack-years, aged between 50 and 80 years. All MAs show statistically significant evidence of reduced mortality in screened patients, although without a reduction in all-cause mortality. However, there was some heterogeneity regarding the age of the target population and the screening follow-up period. Overdiagnosis rates varied between MAs. The SRs and RCT also demonstrated a reduction in lung cancer mortality, but not in all-cause mortality. LDCT lung cancer screening shows a reduction in disease-related mortality, suggesting that the implementation of organized screening for at-risk populations could have a significant positive impact. Some uncertainties remain regarding the best strategy for implementing organized screening programs.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".