{"id":"W4408115324","doi":"10.3390/mi16030299","title":"Transforming Drug Discovery with Miniaturized Predictive Tissue Models","year":2025,"lang":"en","type":"editorial","venue":"Micromachines","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Drug discovery; Pharmacodynamics; Drug; Clinical trial; Drug development; Medicine; Pharmacokinetics; Pharmacology; Intensive care medicine; Efficacy; Computational biology; Computer science; Bioinformatics; Internal medicine; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003879963,0.0006252601,0.0007457384,0.0003761914,0.0001212574,0.0002358292,0.000847687,0.0007445929,0.00002780348],"category_scores_gemma":[0.0002488479,0.0004955947,0.0001334435,0.0004235909,0.000178601,0.0003707067,0.0001481223,0.001936084,0.00001708862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002284345,"about_ca_system_score_gemma":0.0003898135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002414104,"about_ca_topic_score_gemma":0.0001307912,"domain_scores_codex":[0.9971213,0.00007176846,0.0004601526,0.0006061418,0.001050076,0.0006905961],"domain_scores_gemma":[0.9979479,0.001168742,0.00005740936,0.0005205625,0.0001485021,0.0001568486],"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":[0.0001512105,0.00003824773,0.000003011703,0.001560908,0.0003725036,0.00005113194,0.0004521913,0.001238553,0.00640119,0.00001606027,0.9864014,0.003313556],"study_design_scores_gemma":[0.001280576,0.00009223313,0.00000589506,0.001662789,0.0001666844,0.000004007702,0.00002815188,0.007117077,0.01166108,0.0004501521,0.976754,0.0007774255],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.008216739,0.005091703,0.03539964,0.0001961583,0.929521,0.001515628,0.002635865,0.001470832,0.0159524],"genre_scores_gemma":[0.01341032,0.002561732,0.009184958,0.00003200776,0.928772,0.0005103749,0.002812565,0.0005674153,0.04214866],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.02621468,"threshold_uncertainty_score":0.9997495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004337865643187509,"score_gpt":0.2478988159131968,"score_spread":0.2435609502700093,"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."}}