{"id":"W2958155285","doi":"10.1007/s10278-019-00253-9","title":"Deterministic vs. Probabilistic: Best Practices for Patient Matching Based on a Comparison of Two Implementations","year":2019,"lang":"en","type":"article","venue":"Journal of Digital Imaging","topic":"Radiology practices and education","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Integrity Testing Laboratory (Canada)","funders":"","keywords":"Probabilistic logic; Matching (statistics); Computer science; Implementation; Balanced scorecard; Medicine; Data mining; Statistics; Artificial intelligence; Mathematics; Business; Process 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04527235,0.0008026677,0.001332999,0.003033008,0.000905014,0.00458023,0.003280597,0.00353287,0.004925555],"category_scores_gemma":[0.1630322,0.001007249,0.001661189,0.002374774,0.001152896,0.004640386,0.002701569,0.001952702,0.001094037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003381063,"about_ca_system_score_gemma":0.005113945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01034699,"about_ca_topic_score_gemma":0.008755038,"domain_scores_codex":[0.9589065,0.02352796,0.003523726,0.003092223,0.00975886,0.001190831],"domain_scores_gemma":[0.8715488,0.09517375,0.005912691,0.01603094,0.01014303,0.00119079],"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":[0.009301607,0.001504529,0.04148126,0.001666044,0.001736664,0.0001959507,0.001150471,0.2232496,0.004315883,0.02908559,0.01047577,0.6758367],"study_design_scores_gemma":[0.001598618,0.002293263,0.01971647,0.0005144285,0.001179514,0.0009801417,0.0008456456,0.9139435,0.008156538,0.04277064,0.007712415,0.0002888989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.216352,0.004359391,0.7597594,0.003801269,0.0003482426,0.001079329,0.001334414,0.003829016,0.009137001],"genre_scores_gemma":[0.6770416,0.001069515,0.3191431,0.0004485454,0.00007188138,0.0003325456,0.0006808621,0.000459084,0.0007528077],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04527235,"threshold_uncertainty_score":0.239426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07217730546465154,"score_gpt":0.4429286703654112,"score_spread":0.3707513649007597,"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."}}