{"id":"W2088293391","doi":"10.1002/cncr.24343","title":"Conclusions and reflections","year":2009,"lang":"en","type":"article","venue":"Cancer","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"","keywords":"Nomogram; Medicine; Outcome (game theory); Clinical trial; Disease; Risk stratification; Intensive care medicine; Cancer; Oncology; Internal medicine; Mathematical economics","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":[0.01776691,0.001346366,0.000993885,0.001632023,0.002542884,0.009393815,0.004425291,0.0109666,0.06332843],"category_scores_gemma":[0.05781385,0.0003367301,0.001878312,0.001063707,0.004199005,0.008658303,0.005345099,0.01944137,0.04044211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004814377,"about_ca_system_score_gemma":0.01305288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00398564,"about_ca_topic_score_gemma":0.004899951,"domain_scores_codex":[0.9870967,0.002552317,0.0009344363,0.001913842,0.005935327,0.001567482],"domain_scores_gemma":[0.946393,0.0113018,0.00180254,0.004131008,0.02835554,0.008016168],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001115732,0.00007232518,0.0007281916,0.0003384763,0.00002803241,0.000198121,0.0002841904,0.0001303959,0.0001667277,0.01779854,0.9307149,0.04942853],"study_design_scores_gemma":[0.00002560485,0.00004257989,0.00067599,0.0009294194,0.00001990365,0.0003283438,0.0009292447,0.00007973503,0.0003265534,0.02088689,0.9757312,0.00002441299],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0007445464,0.009376829,0.002000095,0.8394794,0.1154658,0.00007627191,0.0007703318,0.0001914417,0.03189526],"genre_scores_gemma":[0.01795748,0.01871489,0.005512042,0.7985966,0.0606901,0.0002038409,0.001069169,0.0002785603,0.09697735],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.06332843,"threshold_uncertainty_score":0.2118547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01997036788727629,"score_gpt":0.3930172524540528,"score_spread":0.3730468845667765,"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."}}