{"id":"W2130840424","doi":"10.18433/j35c8b","title":"Artificial Neural Network Modeling for Drug Dialyzability Prediction","year":2013,"lang":"en","type":"article","venue":"Journal of Pharmacy & Pharmaceutical Sciences","topic":"Analytical Methods in Pharmaceuticals","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Jewish General Hospital","funders":"Université de Montréal; Jewish General Hospital; Fresenius Medical Care North America","keywords":"Dialysis; Drug; Chromatography; Atenolol; Chemistry; Ultrafiltration (renal); Pharmacology; High-performance liquid chromatography; Internal medicine; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0007301227,0.0009216967,0.0007582288,0.0005434314,0.0002608566,0.0007395003,0.0006077358,0.0008971783,0.001665118],"category_scores_gemma":[0.001372443,0.0003038102,0.0007382959,0.0005207006,0.0002119161,0.0004154173,0.0003577761,0.0009177435,0.0003521394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006070281,"about_ca_system_score_gemma":0.0005238191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01037247,"about_ca_topic_score_gemma":0.005510834,"domain_scores_codex":[0.9997737,0.00007850066,0.0000192431,0.00005231297,0.00004892188,0.0000274986],"domain_scores_gemma":[0.9994524,0.0003683202,0.00004671392,0.00001176933,0.0001091612,0.0000116612],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005257645,0.00005193499,0.001367917,0.00006678666,0.00006163622,0.00004089035,0.0000109147,0.9752329,0.0007653609,0.0003424834,0.0003596179,0.02164711],"study_design_scores_gemma":[0.000001396943,0.00001011894,0.0001454559,0.00000441014,0.000004488134,0.000002631542,0.000001417665,0.9994281,0.0001409324,0.0001569099,0.0001022374,0.000001960747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2208191,0.005620605,0.7627661,0.0007681097,0.0002974219,0.0001539531,0.0007470684,0.001146773,0.007680881],"genre_scores_gemma":[0.9383091,0.001460327,0.05438825,0.0001257856,0.00007416591,0.0003015027,0.0006596252,0.00004248402,0.004638903],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01037247,"threshold_uncertainty_score":0.02062416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1774598505730973,"score_gpt":0.4532927471965659,"score_spread":0.2758328966234685,"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."}}