{"id":"W3094027353","doi":"10.2196/20324","title":"Institution-Specific Machine Learning Models for Prehospital Assessment to Predict Hospital Admission: Prediction Model Development Study","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Trauma and Emergency Care Studies","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"RIKEN","keywords":"Medicine; Predictive modelling; Computer science; Medical emergency; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01001129,0.001264951,0.001028684,0.001211336,0.0003695206,0.0008521017,0.001066407,0.00056827,0.001779109],"category_scores_gemma":[0.01613653,0.0003340336,0.001740426,0.001021514,0.0002033176,0.0008758156,0.001014856,0.002122657,0.0004713673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009305732,"about_ca_system_score_gemma":0.002239008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007275248,"about_ca_topic_score_gemma":0.005791234,"domain_scores_codex":[0.997916,0.001333504,0.0001079294,0.0003103968,0.0001809855,0.0001512566],"domain_scores_gemma":[0.9869616,0.008706009,0.0007465049,0.0009403288,0.002128835,0.0005167342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001981061,0.002449156,0.6794406,0.0002604979,0.002169834,0.0003133558,0.0002337209,0.1460693,0.0004221061,0.001086873,0.005921465,0.1596521],"study_design_scores_gemma":[0.0001452733,0.0009669332,0.04797319,0.00009707237,0.0005508421,0.0001645368,0.000105906,0.9474452,0.0006009158,0.0009017201,0.00101333,0.00003509785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9465395,0.001982338,0.04719843,0.0006557966,0.000112474,0.0005033963,0.001144367,0.0002917396,0.001571975],"genre_scores_gemma":[0.9792357,0.0006202877,0.01740583,0.0001050415,0.00006460486,0.0002939426,0.001484592,0.00003230134,0.0007577103],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01001129,"threshold_uncertainty_score":0.05294538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05026349916723954,"score_gpt":0.32132486006233,"score_spread":0.2710613608950905,"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."}}