{"id":"W4390701962","doi":"10.1186/s12967-024-04854-z","title":"Multi-institutional prognostic modeling of survival outcomes in NSCLC patients treated with first-line immunotherapy using radiomics","year":2024,"lang":"en","type":"article","venue":"Journal of Translational Medicine","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre hospitalier de l'Université Laval; Centre Hospitalier de l’Université de Montréal; Université du Québec à Trois-Rivières; Université Laval","funders":"Fonds de Recherche du Québec - Santé; Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval","keywords":"Feature selection; Artificial intelligence; Machine learning; Medicine; Oncology; Radiomics; Computer science; Feature (linguistics); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002671635,0.0004785414,0.0004416274,0.001332479,0.0003436037,0.0008613634,0.0006115127,0.0003758925,0.001089208],"category_scores_gemma":[0.004021382,0.0001702736,0.001072554,0.0006966707,0.0002506208,0.0004597602,0.0005589339,0.0004985197,0.0002162977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001063745,"about_ca_system_score_gemma":0.0008924799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01132528,"about_ca_topic_score_gemma":0.007747105,"domain_scores_codex":[0.9996027,0.0001436177,0.00003452281,0.0001088871,0.00004684935,0.0000634508],"domain_scores_gemma":[0.9981236,0.00084685,0.0004644342,0.0001491159,0.0002659184,0.0001501061],"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":[0.0005230939,0.0001661651,0.8787859,0.00002335048,0.0003213469,0.0001855157,0.00006172495,0.1022052,0.0004367446,0.0001442386,0.0006439358,0.01650273],"study_design_scores_gemma":[0.00002595635,0.0002597815,0.2714629,0.00001579014,0.0001695206,0.0002648323,0.000114756,0.7259313,0.0006920631,0.0006558076,0.0003821314,0.00002514924],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918438,0.0001169853,0.006790787,0.0001602773,0.00001031841,0.00003598638,0.0006001392,0.00007739174,0.0003644309],"genre_scores_gemma":[0.9979886,0.00002567577,0.001225505,0.000007752017,0.000006250623,0.00001609681,0.0006285443,0.000004984353,0.00009659726],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01132528,"threshold_uncertainty_score":0.02251869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03400291794991634,"score_gpt":0.3234476935512806,"score_spread":0.2894447756013643,"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."}}