{"id":"W2298541750","doi":"10.1016/j.jtho.2016.01.021","title":"Computer Vision Tool and Technician as First Reader of Lung Cancer Screening CT Scans","year":2016,"lang":"en","type":"article","venue":"Journal of Thoracic Oncology","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"BC Cancer Agency; Princess Margaret Cancer Centre; University of Calgary; University of Ottawa; Juravinski Hospital; Memorial University of Newfoundland; Vancouver Coastal Health; Institut universitaire de cardiologie et de pneumologie de Québec; University Health Network; Ottawa Hospital; Dalhousie University; Brock University; Toronto General Hospital","funders":"Partenariat Canadien Contre Le Cancer; Terry Fox Research Institute","keywords":"Technician; Medicine; Lung cancer; Radiology; Lung cancer screening; Confidence interval; Computed tomography; Medical physics; Nuclear medicine; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003162714,0.0001091757,0.0004908327,0.000149553,0.00004428161,0.00000640789,0.00006886094,0.00007423228,0.000167591],"category_scores_gemma":[0.000036929,0.00006044283,0.00009867699,0.00009609626,0.00009556652,0.0001053711,0.00004654506,0.0001337616,0.000001885986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004697512,"about_ca_system_score_gemma":0.0002540196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009980451,"about_ca_topic_score_gemma":0.0001019058,"domain_scores_codex":[0.9990219,0.00005194578,0.0004236473,0.0001346841,0.0002079764,0.0001598483],"domain_scores_gemma":[0.9989198,0.0002066629,0.0004315886,0.0001335587,0.0001953511,0.0001130036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001304857,0.0003872031,0.1565624,0.0001786665,0.0004760607,0.0006477479,0.000164665,0.00001681893,0.0014475,0.00004174392,0.01455087,0.8242214],"study_design_scores_gemma":[0.009800841,0.02132981,0.678687,0.01607062,0.00346088,0.008707527,0.000222517,0.0005846494,0.0226131,0.0002234058,0.2378394,0.0004602996],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9621699,0.002403624,0.001819584,0.03235009,0.0004375235,0.0002395799,0.000009625624,0.00001040013,0.0005596556],"genre_scores_gemma":[0.9907296,0.001986018,0.006037421,0.0005616394,0.0004422126,0.000008155705,5.56681e-7,0.00001451869,0.0002199203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8237612,"threshold_uncertainty_score":0.2464787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01801706518436059,"score_gpt":0.4195248642971166,"score_spread":0.401507799112756,"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."}}