{"id":"W2148802044","doi":"10.1002/cncr.10492","title":"TNM residual tumor classification revisited","year":2002,"lang":"en","type":"article","venue":"Cancer","topic":"Gastric Cancer Management and Outcomes","field":"Medicine","cited_by":491,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Medicine; Residual; Standardization; Disease; Classification scheme; Intensive care medicine; Completeness (order theory); Oncology; Internal medicine; Machine learning; Computer science; Algorithm","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":[0.008366164,0.0004367795,0.0003515946,0.002620091,0.0003564826,0.00119617,0.001619774,0.0006177883,0.001501753],"category_scores_gemma":[0.01407388,0.00006855728,0.0003488338,0.00289709,0.001017152,0.001220932,0.0007827108,0.001863058,0.0006152338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001148818,"about_ca_system_score_gemma":0.001909837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003931968,"about_ca_topic_score_gemma":0.004424579,"domain_scores_codex":[0.9976215,0.00106123,0.0003039848,0.0001822104,0.0006825303,0.0001486424],"domain_scores_gemma":[0.9954242,0.001561575,0.0007294783,0.0002615589,0.001733275,0.0002899294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003113406,0.00003883554,0.08172514,0.0008500874,0.00008065366,0.001538524,0.0009075319,0.001980039,0.002121108,0.06232138,0.08112593,0.7669994],"study_design_scores_gemma":[0.0001164395,0.0006656125,0.1325516,0.003667589,0.0003230704,0.0364811,0.002419795,0.01822376,0.002705716,0.1190467,0.6836802,0.000118499],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.2432611,0.275146,0.1838786,0.1404502,0.02263149,0.0006331601,0.004164757,0.001262013,0.1285727],"genre_scores_gemma":[0.8001574,0.04344536,0.1178008,0.01362042,0.008470597,0.0004961147,0.003677241,0.0003159867,0.01201618],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008366164,"threshold_uncertainty_score":0.04424506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06796921168696672,"score_gpt":0.3116086353482219,"score_spread":0.2436394236612552,"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."}}