{"id":"W6960159768","doi":"10.13026/s5rz-1j65","title":"Asclepius-R : Clinical Large Language Model Built On MIMIC-III Discharge Summaries","year":2024,"lang":"en","type":"dataset","venue":"PhysioNet","topic":"Phytochemistry and Biological Activities","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Usability; Resource (disambiguation); Language model; Benchmark (surveying); Data modeling","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":[0.001953385,0.001765841,0.0005610992,0.001565444,0.0003510184,0.001105536,0.001780896,0.001250029,0.01028816],"category_scores_gemma":[0.01253233,0.0004197385,0.001451548,0.00106417,0.0003474445,0.001088534,0.001347213,0.001740564,0.008138789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001281613,"about_ca_system_score_gemma":0.002845306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01226245,"about_ca_topic_score_gemma":0.02434348,"domain_scores_codex":[0.998867,0.0004346098,0.000184592,0.000304821,0.0001345723,0.00007445338],"domain_scores_gemma":[0.9964108,0.002218364,0.0001791554,0.0005104584,0.0005034803,0.0001777351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001803646,0.0006821537,0.02487121,0.002448875,0.0004574469,0.001756975,0.0005645839,0.05853282,0.005708527,0.003897808,0.6945102,0.2047658],"study_design_scores_gemma":[0.001798681,0.00139494,0.02393196,0.0008973253,0.0004290965,0.002992574,0.001175437,0.5612769,0.0197441,0.01948849,0.3664565,0.0004140607],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1050128,0.002138561,0.07953645,0.004002163,0.001090824,0.001476688,0.7446781,0.05444836,0.007616007],"genre_scores_gemma":[0.1231588,0.0006332099,0.06391034,0.0009149756,0.0001246656,0.00149338,0.8043425,0.0007887985,0.004633376],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01226245,"threshold_uncertainty_score":0.03441727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04396920669721984,"score_gpt":0.3136160494600683,"score_spread":0.2696468427628484,"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."}}