{"id":"W4408828175","doi":"10.70477/lntm6092","title":"HUMAN-LIKE CANCER TISSUE MODELS AS A DRUG SCREENING PLATFORM","year":2024,"lang":"en","type":"article","venue":"","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cancer; Drug; Computer science; Cancer drugs; Medicine; Internal medicine; Pharmacology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006594437,0.0005108334,0.000348205,0.0006430541,0.0001768031,0.000482381,0.0004871452,0.0006452629,0.003427344],"category_scores_gemma":[0.00024492,0.000227674,0.0003796431,0.0003128618,0.0002421843,0.0002695234,0.0003248692,0.0007051455,0.001496727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003636972,"about_ca_system_score_gemma":0.0003988529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001328253,"about_ca_topic_score_gemma":0.002066829,"domain_scores_codex":[0.9995952,0.00008513188,0.00001789852,0.00005361204,0.0002031696,0.0000450277],"domain_scores_gemma":[0.9998094,0.00005335174,0.00002222291,0.00003579066,0.00003841726,0.00004086834],"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":[0.000150209,0.0001893586,0.0003170049,0.0001348533,0.00001732774,0.000197772,0.00003498988,0.001799179,0.987382,0.001199041,0.001933573,0.006644646],"study_design_scores_gemma":[0.0000460988,0.0008652556,0.001186933,0.00002876912,0.00003844979,0.0006671979,0.00002808964,0.004326455,0.9689167,0.0002227602,0.023657,0.00001629278],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7238015,0.01231165,0.1876073,0.00158511,0.0005655159,0.002339036,0.01320483,0.004564588,0.05402057],"genre_scores_gemma":[0.8823338,0.006588692,0.07684348,0.0007849029,0.00004038728,0.001254956,0.008185666,0.0002084489,0.02375976],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003427344,"threshold_uncertainty_score":0.01146567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04713060775871605,"score_gpt":0.3508513062729723,"score_spread":0.3037206985142563,"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."}}