{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005569648,0.0005125219,0.0004461618,0.001547006,0.0003045645,0.000514854,0.0005157462,0.0007267288,0.001144827],"category_scores_gemma":[0.01038607,0.0001611999,0.0005639419,0.0006333194,0.0003580456,0.000930164,0.0003661817,0.0003393995,0.000297061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004522523,"about_ca_system_score_gemma":0.0007208672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001591093,"about_ca_topic_score_gemma":0.001021031,"domain_scores_codex":[0.9979144,0.001039557,0.0001075253,0.0002587617,0.0005664417,0.0001133203],"domain_scores_gemma":[0.9927746,0.005020645,0.000284914,0.0002141368,0.001610754,0.0000950676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00244916,0.002478149,0.3072044,0.0006550504,0.0002275186,0.000678078,0.0006339924,0.05654554,0.04511899,0.002019148,0.001667561,0.5803224],"study_design_scores_gemma":[0.0001420275,0.003281829,0.07519486,0.00008262032,0.0001980298,0.001078065,0.0006196982,0.8993843,0.01730733,0.001585923,0.001050657,0.00007473056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7069289,0.0007088694,0.2882252,0.0003294525,0.00005814982,0.0009380959,0.0002764069,0.0002946979,0.002240201],"genre_scores_gemma":[0.9025783,0.0001266702,0.09643646,0.00004379113,0.00002022169,0.0002941555,0.000148991,0.00000709821,0.0003443582],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005569648,"threshold_uncertainty_score":0.02945548,"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."}}