{"id":"W2604204979","doi":"10.3233/978-1-61499-742-9-1","title":"What We Can Learn from Amazon for Clinical Decision Support Systems","year":2017,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; SNC-Lavalin (Canada)","funders":"","keywords":"Amazon rainforest; Computer science; Clinical decision support system; Data science; Decision support system; Artificial intelligence; Biology","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.003820285,0.0002183353,0.0009443973,0.0003426531,0.003608165,0.00004163858,0.0005314104,0.0008262857,0.00001082963],"category_scores_gemma":[0.006569316,0.0001827737,0.0000509196,0.000154796,0.0009969779,0.0005421602,0.0006734763,0.001412724,0.00007031885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002971576,"about_ca_system_score_gemma":0.0005280971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009079542,"about_ca_topic_score_gemma":0.006633323,"domain_scores_codex":[0.9953102,0.0002019658,0.003176344,0.0002657592,0.0001857375,0.0008599655],"domain_scores_gemma":[0.9937024,0.003128205,0.001650789,0.0009544251,0.0004102507,0.0001539939],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001687439,0.00004254114,0.5389875,0.00375776,0.00006410823,0.000004521486,0.03270904,0.0000105627,1.215222e-7,0.01910819,0.01977839,0.3853686],"study_design_scores_gemma":[0.001098068,0.001146343,0.009924962,0.00724899,0.00001842526,0.00000477703,0.5839658,0.007608212,0.000005465824,0.06311195,0.3255232,0.0003438533],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8180571,0.03609107,0.001911623,0.1041663,0.03165173,0.00695081,0.0001919977,0.0004325541,0.0005468997],"genre_scores_gemma":[0.739908,0.2496875,0.005511133,0.003162928,0.0004600659,0.0008023086,0.0000228737,0.00003204907,0.0004131706],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5512567,"threshold_uncertainty_score":0.997689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4294078278936738,"score_gpt":0.6091081733350611,"score_spread":0.1797003454413873,"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."}}