{"id":"W2968081492","doi":"10.2196/13476","title":"A Machine Learning Method for Identifying Lung Cancer Based on Routine Blood Indices: Qualitative Feasibility Study","year":2019,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Lung cancer; Medicine; Cancer; Lung; Internal medicine; Oncology; Intensive care medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005793345,0.0003434001,0.0008581863,0.000293476,0.0001969063,0.00008028787,0.0003208179,0.0002255354,0.001024719],"category_scores_gemma":[0.002666635,0.0002585281,0.0002357708,0.000423823,0.00008781185,0.0001967982,0.0001166226,0.001802552,0.00002792014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000179797,"about_ca_system_score_gemma":0.0003945508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002019687,"about_ca_topic_score_gemma":0.00001421301,"domain_scores_codex":[0.9957433,0.0004416274,0.001168291,0.000338192,0.001744515,0.0005640368],"domain_scores_gemma":[0.9965551,0.001704576,0.0004980974,0.0004519368,0.0002049367,0.0005852883],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002270799,0.005102293,0.7281245,0.0102171,0.001586185,0.0001019603,0.1898113,0.003008204,0.0002066059,0.0007384562,0.001747364,0.0570852],"study_design_scores_gemma":[0.01302868,0.001655716,0.006568933,0.001094761,0.0003906543,0.00002059115,0.01423105,0.961184,0.00004954837,0.00004804783,0.001413357,0.0003146449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8308773,0.0001554577,0.1595291,0.001875074,0.000501489,0.004538609,0.00003049793,0.0002622585,0.002230179],"genre_scores_gemma":[0.939782,0.00002086764,0.05401618,0.004608859,0.0002490005,0.0004463851,0.0001802379,0.00006431113,0.0006321865],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9581758,"threshold_uncertainty_score":0.9999867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04071611167170215,"score_gpt":0.4582163525879367,"score_spread":0.4175002409162346,"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."}}