{"id":"W3144747894","doi":"10.1164/rccm.202007-2791oc","title":"Machine Learning for Early Lung Cancer Identification Using Routine Clinical and Laboratory Data","year":2021,"lang":"en","type":"article","venue":"American Journal of Respiratory and Critical Care Medicine","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":176,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"","keywords":"Medicine; Lung cancer; Identification (biology); Medical physics; Intensive care medicine; Artificial intelligence; Machine learning; Oncology","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":[0.0008695252,0.0001393625,0.0006324466,0.00007802929,0.0001263603,0.00002553591,0.00007561738,0.00004891936,0.0000352406],"category_scores_gemma":[0.001801688,0.0001030004,0.00005840754,0.000199091,0.0007368243,0.0001483481,0.00006144798,0.0003337755,1.377205e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007562905,"about_ca_system_score_gemma":0.0003821971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005893175,"about_ca_topic_score_gemma":0.00001460242,"domain_scores_codex":[0.9983289,0.0002291888,0.0006534507,0.0003215839,0.0002873012,0.0001796092],"domain_scores_gemma":[0.9975726,0.0005767894,0.0002407596,0.0002580716,0.0009936669,0.0003581021],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003835906,0.00009472995,0.9290407,0.0003978193,0.0002243912,0.000240305,0.0004129118,0.000002757682,0.0007356774,0.0001561586,0.0002988164,0.06801216],"study_design_scores_gemma":[0.01846531,0.01673388,0.8773665,0.004922576,0.01019826,0.0007335532,0.01243062,0.003188394,0.00207407,0.00009520525,0.05315682,0.0006348283],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8984387,0.09455954,0.00160563,0.004831646,0.0003322073,0.0001398316,0.00006371974,0.000008897464,0.00001981147],"genre_scores_gemma":[0.993935,0.002239774,0.0008664943,0.001886916,0.001016666,0.000006239706,0.00001878984,0.00002061696,0.000009484602],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0954963,"threshold_uncertainty_score":0.4200236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0648158238050489,"score_gpt":0.4423355480819023,"score_spread":0.3775197242768534,"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."}}