{"id":"W2111769930","doi":"10.1016/j.compbiomed.2004.07.007","title":"Hypoplastic left heart syndrome: knowledge discovery with a data mining approach","year":2004,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hospital for Sick Children; University of Toronto; SickKids Foundation","funders":"","keywords":"Hypoplastic left heart syndrome; Data collection; Data mining; Computer science; Metric (unit); Measure (data warehouse); Knowledge extraction; Medicine; Data acquisition; Machine learning; Artificial intelligence; Medical physics; Heart disease; Statistics; Internal medicine; Operations management","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000484403,0.0001711852,0.0004121253,0.0002196574,0.00008185031,0.00003207321,0.0006572197,0.0000887887,0.00000133208],"category_scores_gemma":[0.00006821365,0.0001217475,0.00001284096,0.0002805936,0.0003419264,0.0005973447,0.0005451696,0.0001841496,0.000005495525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003884315,"about_ca_system_score_gemma":0.0001036229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005713962,"about_ca_topic_score_gemma":0.00008204904,"domain_scores_codex":[0.99862,0.0001269115,0.0002412534,0.0006647249,0.00008141366,0.0002656849],"domain_scores_gemma":[0.9987109,0.0004780744,0.00007087096,0.0006155436,0.00002988601,0.00009470695],"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":[0.0004930015,0.00293756,0.3378384,0.001336457,0.0009729723,0.001519381,0.03957241,0.000494069,0.003201994,0.07637796,0.01088036,0.5243754],"study_design_scores_gemma":[0.0583787,0.01560714,0.5321745,0.0138673,0.0003153038,0.08426964,0.004275414,0.2136188,0.0002658326,0.02326346,0.04865255,0.005311393],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1728437,0.00119426,0.8233237,0.001252722,0.0006983961,0.0001953648,0.000004256135,0.00007249937,0.0004150971],"genre_scores_gemma":[0.9694063,0.00003056031,0.02996224,0.0004102667,0.0001058841,0.00000768921,0.00004124783,0.000006614915,0.00002919387],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7965626,"threshold_uncertainty_score":0.4964717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05144625212388019,"score_gpt":0.3126340393951114,"score_spread":0.2611877872712312,"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."}}