{"id":"W3203435004","doi":"10.1101/2021.09.27.21263258","title":"Survival prediction with Bayesian Networks in more than 6000 non-small cell lung cancer patients","year":2021,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"","keywords":"Lung cancer; Bayesian probability; Medicine; Radiation therapy; Stage (stratigraphy); Bayesian network; Lung function; Cancer; Oncology; Computer science; Medical physics; Internal medicine; Lung; Artificial intelligence; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002171872,0.0005504985,0.000606492,0.0009664918,0.0002401066,0.000601651,0.0005016673,0.0006477855,0.001604985],"category_scores_gemma":[0.007054016,0.0003343551,0.0007406888,0.001040729,0.0001926826,0.0005806023,0.0003904166,0.0007855585,0.0004278539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009151978,"about_ca_system_score_gemma":0.000641163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02202207,"about_ca_topic_score_gemma":0.01719808,"domain_scores_codex":[0.999326,0.0003045021,0.00004312631,0.0001761833,0.0000930053,0.00005716806],"domain_scores_gemma":[0.997012,0.002069962,0.0002825275,0.0002407761,0.000246081,0.0001487128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001246099,0.0007772435,0.4030458,0.0001534838,0.0004525315,0.0004305679,0.0001764403,0.4806621,0.001075243,0.00139533,0.01024457,0.1003406],"study_design_scores_gemma":[0.00008104517,0.0001857258,0.04781104,0.00003290112,0.00007341657,0.0001058201,0.00006846148,0.9452101,0.0006806936,0.003281295,0.002446825,0.00002276308],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.962727,0.001033665,0.01962678,0.001389186,0.00004513828,0.00008435563,0.01356632,0.0003081163,0.00121949],"genre_scores_gemma":[0.9726887,0.0003107036,0.009711091,0.0001264098,0.00004141328,0.00006220684,0.01635307,0.00001550869,0.0006910068],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02202207,"threshold_uncertainty_score":0.04378778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008850406206248326,"score_gpt":0.2531626695020834,"score_spread":0.2443122632958351,"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."}}