{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002439854,0.0003581168,0.0003237789,0.0002952703,0.0001450733,0.000255994,0.0002347248,0.0003684016,0.001438504],"category_scores_gemma":[0.0002659327,0.0002327265,0.0002572249,0.0001970912,0.0001893024,0.0002614986,0.0002553899,0.0004153643,0.0005488618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003641534,"about_ca_system_score_gemma":0.0003195068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004270992,"about_ca_topic_score_gemma":0.001272304,"domain_scores_codex":[0.9998063,0.00003100127,0.00001531183,0.00005388861,0.00007342092,0.00002010297],"domain_scores_gemma":[0.9998325,0.00006924236,0.00003628924,0.00002194259,0.00002404225,0.00001599239],"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":[0.00002843343,0.00001577439,0.00006911371,0.00005686641,0.000005095734,0.00002721887,0.00001219878,0.0006309762,0.9940064,0.0003569065,0.0004554246,0.004335531],"study_design_scores_gemma":[0.00001575698,0.0001352536,0.0007384,0.000008039333,0.00001053639,0.00008566036,0.00001109957,0.007847114,0.983326,0.0001781674,0.007632568,0.00001152299],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5056818,0.01411146,0.4485357,0.001970972,0.001261757,0.0008473393,0.004979888,0.004931164,0.01768004],"genre_scores_gemma":[0.7449442,0.004498382,0.2417317,0.0005351916,0.0001173912,0.0006544557,0.001256886,0.0001318516,0.006130057],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001438504,"threshold_uncertainty_score":0.004812241,"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."}}