{"id":"W2185644008","doi":"10.1039/c5lc01108f","title":"Micro-dissected tumor tissues on chip: an ex vivo method for drug testing and personalized therapy","year":2015,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"3D Printing in Biomedical Research","field":"Engineering","cited_by":189,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Centre Hospitalier de l’Université de Montréal","funders":"Canadian Cancer Society Research Institute; Prostate Cancer Canada","keywords":"Ex vivo; Drug; Personalized medicine; In vivo; Medicine; Chip; Pharmacology; Biomedical engineering; Biology; Computer science; 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":[0.0005603907,0.0006127984,0.0004728718,0.0004646154,0.0002340066,0.0006725736,0.0006854907,0.0007270777,0.001586366],"category_scores_gemma":[0.0003714765,0.0003721208,0.0003821868,0.0002341544,0.0003983078,0.0004242158,0.0004650105,0.0008802249,0.001147829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000376054,"about_ca_system_score_gemma":0.0004352941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004899267,"about_ca_topic_score_gemma":0.001196673,"domain_scores_codex":[0.9995263,0.00007520681,0.00002770411,0.000126818,0.0002052894,0.00003867447],"domain_scores_gemma":[0.9996042,0.0001677256,0.00006103526,0.00007854831,0.00005606468,0.00003251134],"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":[0.00003394778,0.0000388714,0.0002116561,0.0001575239,0.00001848251,0.00006013009,0.00002206576,0.0006116946,0.9813737,0.0006326617,0.0009277052,0.01591152],"study_design_scores_gemma":[0.00000979586,0.0001360387,0.001343251,0.00001475773,0.00002764753,0.0003629706,0.000016704,0.005523473,0.9776176,0.000302877,0.01461721,0.00002765914],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1193632,0.01693812,0.8466812,0.001507304,0.000859317,0.0004986264,0.00332899,0.003091216,0.007732037],"genre_scores_gemma":[0.3784201,0.01196985,0.5956783,0.001206691,0.000234199,0.001355503,0.001761577,0.0002601952,0.009113624],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001586366,"threshold_uncertainty_score":0.005306959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06657977835010595,"score_gpt":0.3523151289803462,"score_spread":0.2857353506302402,"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."}}