{"id":"W3205062185","doi":"10.1002/adhm.202101085","title":"Microfluidic Arrays of Breast Tumor Spheroids for Drug Screening and Personalized Cancer Therapies","year":2021,"lang":"en","type":"article","venue":"Advanced Healthcare Materials","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":116,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network; Canada Research Chairs; University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Spheroid; Breast cancer; Personalized medicine; In vivo; Cancer research; Cancer; Drug; In vitro; Drug development; Medicine; Limiting; Oncology; Pharmacology; Biology; Internal medicine; Bioinformatics; Biotechnology","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0003585252,0.0001547423,0.0003920703,0.00004503487,0.00008149946,0.00003302122,0.0001137263,0.00005737344,0.0002230726],"category_scores_gemma":[0.00009630756,0.0001508176,0.00004312134,0.0001387613,0.0001154831,0.00007830978,0.00005481796,0.0000934612,0.00000159351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005354533,"about_ca_system_score_gemma":0.00009397573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001720423,"about_ca_topic_score_gemma":0.00003843254,"domain_scores_codex":[0.998709,0.00008364998,0.0003553638,0.0002437077,0.0002037726,0.0004044869],"domain_scores_gemma":[0.9992183,0.0001965663,0.00005891607,0.0001860563,0.0002069238,0.0001332112],"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":[0.00008947473,0.000008189046,0.0002781515,0.002015121,0.00004727185,0.000004800835,0.0005153584,0.00001132451,0.9662089,0.0003508615,0.0003436838,0.03012691],"study_design_scores_gemma":[0.001089601,0.00003257241,0.002358277,0.0006619868,0.00001109342,0.00002712675,0.000749586,0.00006982572,0.9823713,0.0009697881,0.01144458,0.0002142322],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9568225,0.0353892,0.004119869,0.002048289,0.0005011756,0.0003981092,0.0005732259,0.0001258233,0.00002182414],"genre_scores_gemma":[0.9700297,0.007874726,0.0212861,0.0001958535,0.0001927322,0.0001984526,0.00004587763,0.00007184796,0.0001047176],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02991268,"threshold_uncertainty_score":0.6150163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02125752412526893,"score_gpt":0.3151391385005404,"score_spread":0.2938816143752715,"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."}}