{"id":"W3021554968","doi":"10.51893/2020.2.ed2","title":"Opportunities and challenges of clustering, crossing over, and using registry data in the PEPTIC trial","year":2020,"lang":"en","type":"article","venue":"Critical Care and Resuscitation","topic":"Antibiotics Pharmacokinetics and Efficacy","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta Hospital","funders":"","keywords":"Medicine; Cluster analysis; Peptic; Data mining; Peptic ulcer; Internal medicine; Artificial intelligence; Computer science","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.8255687,0.002552432,0.01423141,0.009195458,0.006540582,0.02276947,0.01329039,0.02066317,0.005920775],"category_scores_gemma":[0.8995237,0.004089303,0.00985782,0.01493747,0.01561529,0.02365404,0.01540057,0.02491674,0.002166986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008100911,"about_ca_system_score_gemma":0.02959911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007649409,"about_ca_topic_score_gemma":0.009093914,"domain_scores_codex":[0.0797478,0.8089423,0.06959533,0.01026529,0.02936156,0.002087777],"domain_scores_gemma":[0.02462004,0.8941187,0.02490486,0.02776713,0.02472028,0.003869022],"domain_codex":"methods","domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.01116467,0.0003667217,0.01637785,0.03674401,0.02148353,0.000728113,0.009737813,0.00306161,0.0004878693,0.08956087,0.3680358,0.4422511],"study_design_scores_gemma":[0.01630063,0.00360498,0.022228,0.09513562,0.02211961,0.001741884,0.004307492,0.01516707,0.00159166,0.3046253,0.5110895,0.002088247],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.007922317,0.112249,0.04549685,0.7374341,0.08365893,0.003212886,0.002868177,0.0005916042,0.006565925],"genre_scores_gemma":[0.1064806,0.04446939,0.1762855,0.4697989,0.1762353,0.02039071,0.002329717,0.001547496,0.002462526],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8255687,"threshold_uncertainty_score":0.2151049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3210667330013214,"score_gpt":0.4196402740994044,"score_spread":0.09857354109808297,"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."}}