{"id":"W2991037649","doi":"10.1016/j.apt.2019.10.031","title":"Pharmaceutical formulation and manufacturing using particle/powder technology for personalized medicines","year":2019,"lang":"en","type":"article","venue":"Advanced Powder Technology","topic":"Drug Solubulity and Delivery Systems","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Institute of Infection and Immunity; Japan Society for the Promotion of Science","keywords":"Pharmaceutical manufacturing; Process analytical technology; 3D printing; Manufacturing engineering; Process engineering; Advanced manufacturing; Nanotechnology; Computer science; Materials science; Engineering; Mechanical engineering; Work in process; Medicine; Operations management","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004346946,0.0003514693,0.000569838,0.0004287913,0.0003236536,0.00001721047,0.0002900329,0.0009340972,0.0003259686],"category_scores_gemma":[0.0001225633,0.0003411764,0.00009340665,0.0003733171,0.0005178814,0.00029558,0.0001685937,0.0007484414,0.00008809457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001026758,"about_ca_system_score_gemma":0.00005003058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004564589,"about_ca_topic_score_gemma":0.000006556325,"domain_scores_codex":[0.9977243,0.00009835265,0.0005263195,0.0006623867,0.0001536322,0.0008349876],"domain_scores_gemma":[0.9987913,0.0003592712,0.0002075538,0.0003472676,0.000134715,0.0001598961],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00144633,0.000308159,0.05454164,0.0005533807,0.0004932042,0.00003957831,0.0006662416,0.001336862,0.7653508,0.07158508,0.0003387373,0.1033401],"study_design_scores_gemma":[0.009842279,0.0004428026,0.000381718,0.00009193658,0.0003129932,0.0003675295,0.001993956,0.02777569,0.5429879,0.01492368,0.4001597,0.0007198121],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9855198,0.003211193,0.001995593,0.005591128,0.001214352,0.001503769,0.00002913527,0.0005845607,0.0003505236],"genre_scores_gemma":[0.995108,0.0002115685,0.002144968,0.00136373,0.000117123,0.0001593705,0.00001689211,0.00004900671,0.0008293361],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.399821,"threshold_uncertainty_score":0.999904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07480227695100171,"score_gpt":0.4206031741230886,"score_spread":0.3458008971720868,"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."}}