{"id":"W4367276773","doi":"10.1007/978-3-031-26908-0_8","title":"3D Printing for Localized Cancer Therapy","year":2023,"lang":"en","type":"book-chapter","venue":"Advanced clinical pharmacy - research, development and practical applications/Advanced clinical pharmacy - research, development and practical applications","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Limiting; Medicine; Drug delivery; Personalized medicine; Drug; Disease; Cancer treatment; Cancer; Intensive care medicine; Bioinformatics; Pharmacology; Internal medicine; Nanotechnology; Biology; Engineering; Materials 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":[],"consensus_categories":[],"category_scores_codex":[0.0002093371,0.0008748073,0.0007017095,0.0008316067,0.0003414187,0.001481929,0.0008356033,0.001316031,0.03465264],"category_scores_gemma":[0.0001893497,0.0005667461,0.0007689869,0.0009558043,0.0004716657,0.001082188,0.001057221,0.001406915,0.01483072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006005352,"about_ca_system_score_gemma":0.0002781863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004575198,"about_ca_topic_score_gemma":0.001139583,"domain_scores_codex":[0.9997678,0.00001703755,0.000006826564,0.00002999818,0.0001607198,0.00001753584],"domain_scores_gemma":[0.9999379,0.00002973889,0.000006170124,0.00001217501,0.00001047583,0.000003479388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007621598,0.00007999044,0.0000616771,0.001853677,0.00003740609,0.000445457,0.0002077856,0.007448772,0.2542371,0.04855121,0.08469871,0.6023021],"study_design_scores_gemma":[0.00001533113,0.000101075,0.0002987199,0.0003486948,0.00004141546,0.002087761,0.00004271481,0.007168279,0.1230887,0.01383935,0.8529047,0.00006330625],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.008838289,0.1944922,0.2327092,0.001365038,0.004547384,0.0001541762,0.001042866,0.003645362,0.5532054],"genre_scores_gemma":[0.0595989,0.1102396,0.07416683,0.001954641,0.0007773716,0.0002529087,0.0008069748,0.001059796,0.7511429],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03465264,"threshold_uncertainty_score":0.1159247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4227416933930823,"score_gpt":0.5838303113877233,"score_spread":0.161088617994641,"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."}}