{"id":"W3145866246","doi":"10.1002/ijc.33578","title":"Personalising lung cancer screening: An overview of risk‐stratification opportunities and challenges","year":2021,"lang":"en","type":"review","venue":"International Journal of Cancer","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"National Cancer Institute; Horizon 2020 Framework Programme","keywords":"Lung cancer screening; Medicine; Lung cancer; Cancer screening; Population; Risk stratification; Risk assessment; Intensive care medicine; Risk analysis (engineering); Cancer; Environmental health; Oncology; Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002912637,0.0002405856,0.001215774,0.0002459427,0.00003022833,0.00004979711,0.0001730242,0.0001227271,0.0003522855],"category_scores_gemma":[0.00004822518,0.0001789134,0.0004202839,0.00006737598,0.00006428253,0.000201489,0.00003428942,0.0002695857,1.441045e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004910587,"about_ca_system_score_gemma":0.001190853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003188008,"about_ca_topic_score_gemma":0.0001735026,"domain_scores_codex":[0.9980614,0.0001298208,0.0007888112,0.0002190175,0.0006905764,0.0001103951],"domain_scores_gemma":[0.9968697,0.0001609457,0.001594293,0.0001514118,0.00108203,0.0001416181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000381648,0.0001336726,0.0006710716,0.005632467,0.004103985,0.0001126265,0.0004209876,0.000005633462,4.870427e-7,0.0009010018,0.000286514,0.9876934],"study_design_scores_gemma":[0.0008995006,0.0001256039,0.001662164,0.1078512,0.006940192,0.00035875,0.0004119252,0.00002520369,0.00001395167,0.00002697709,0.8814977,0.0001867882],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002545248,0.9963286,0.00001626471,0.002125276,0.0007277289,0.0001745359,0.0002645276,0.000004516155,0.0001040616],"genre_scores_gemma":[0.0007278715,0.9975287,0.0002908183,0.00007382312,0.001184036,0.00004914122,0.00006213986,0.00003322107,0.00005029652],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9875066,"threshold_uncertainty_score":0.7295877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3573408766132797,"score_gpt":0.4915044598762989,"score_spread":0.1341635832630191,"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."}}