{"id":"W4408149466","doi":"10.1016/j.jacr.2025.02.042","title":"Comparing Artificial Intelligence and Traditional Regression Models in Lung Cancer Risk Prediction Using A Systematic Review and Meta-Analysis","year":2025,"lang":"en","type":"review","venue":"Journal of the American College of Radiology","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal University Hospital; University of Saskatchewan","funders":"","keywords":"Meta-regression; Meta-analysis; Lung cancer; Artificial intelligence; Computer science; Regression analysis; Machine learning; Medicine; Oncology; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","metaepi_broad"],"consensus_categories":[],"category_scores_codex":[0.0500472,0.003828202,0.02001276,0.01098095,0.0007540018,0.004822153,0.003376722,0.002769928,0.002348391],"category_scores_gemma":[0.1056454,0.001753073,0.05706316,0.01009709,0.0009860425,0.003297877,0.00175398,0.002744368,0.0003105087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00348258,"about_ca_system_score_gemma":0.005048201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007225427,"about_ca_topic_score_gemma":0.01061242,"domain_scores_codex":[0.9634403,0.02257631,0.007809569,0.002430181,0.003279177,0.000464464],"domain_scores_gemma":[0.8984615,0.08627615,0.008312615,0.002410144,0.004133184,0.0004063908],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.001247071,0.00003504255,0.00582955,0.2706339,0.7047678,0.0000959673,0.00008054566,0.001564777,0.0001071381,0.0002222843,0.0004439235,0.01497194],"study_design_scores_gemma":[0.0005359039,0.0002402213,0.002455408,0.02774877,0.9662882,0.00007406301,0.0000341458,0.001155165,0.0000840123,0.0004206639,0.0009339296,0.00002946065],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.004619428,0.9906424,0.002803146,0.000379118,0.0001927053,0.0006583906,0.0003955874,0.00005083171,0.0002583738],"genre_scores_gemma":[0.2371485,0.7432096,0.01238131,0.001238156,0.000584451,0.003855838,0.00120032,0.00006454357,0.0003172193],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9799873,"threshold_uncertainty_score":0.2646781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1390619711841801,"score_gpt":0.3935945862124272,"score_spread":0.2545326150282471,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}