{"id":"W2535673986","doi":"10.1109/tencon.1993.320144","title":"Use of neural network analysis to diagnose breast cancer patients","year":2002,"lang":"en","type":"article","venue":"","topic":"AI in cancer detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency","funders":"","keywords":"Artificial neural network; Breast cancer; Linear discriminant analysis; Logistic regression; Artificial intelligence; Computer science; Multivariate statistics; Set (abstract data type); Statistical analysis; Cancer; Pattern recognition (psychology); Machine learning; Statistics; Medicine; Internal medicine; Mathematics","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":[0.000964754,0.0004262753,0.0003107156,0.001486628,0.0001620963,0.0005592614,0.0002704957,0.0004367201,0.001019462],"category_scores_gemma":[0.005859863,0.0001118831,0.0002177277,0.0004293081,0.0001411087,0.0004287018,0.0002317412,0.0003004633,0.0002773479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004012115,"about_ca_system_score_gemma":0.0002726803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003422284,"about_ca_topic_score_gemma":0.00280522,"domain_scores_codex":[0.9995275,0.0002487765,0.00003622099,0.00007160077,0.00007713163,0.00003882411],"domain_scores_gemma":[0.9986331,0.0008755758,0.0001089377,0.00005625593,0.0002844184,0.00004173121],"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":[0.002537691,0.0005699781,0.2333858,0.0001808063,0.0003767573,0.0005986915,0.0002670567,0.1417264,0.01193368,0.001512358,0.004027824,0.602883],"study_design_scores_gemma":[0.00004060123,0.0001617665,0.02526893,0.00003000275,0.00007846734,0.0002202535,0.00008810734,0.9659992,0.005596701,0.001564262,0.0009267079,0.00002503627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8919266,0.001024941,0.09764002,0.001068445,0.0001367418,0.000118136,0.0005162948,0.0007710655,0.006797874],"genre_scores_gemma":[0.977797,0.000170275,0.02084197,0.00007549964,0.00002666258,0.00004519517,0.0002820646,0.00001085688,0.0007506017],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003422284,"threshold_uncertainty_score":0.006804764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02514335091316833,"score_gpt":0.2423010028230637,"score_spread":0.2171576519098953,"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."}}