{"id":"W2900174653","doi":"10.1093/neuonc/noy148.1174","title":"CADD-35. THE DEVELOPMENT OF PERSONALIZED CAM AVATAR MODEL TO PREDICT CHEMOTHERAPEUTIC DRUG SENSITIVITY/RESISTANCE OF GLIOMAS","year":2018,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier Universitaire de Sherbrooke; Université de Sherbrooke","funders":"","keywords":"In ovo; Medicine; Glioma; Ex vivo; Temozolomide; In vivo; Oncology; Chorioallantoic membrane; Drug; Precision medicine; Personalized medicine; Cancer research; Internal medicine; Pathology; Bioinformatics; Pharmacology; Biology; Embryo; Angiogenesis","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003877834,0.0005068408,0.0003752813,0.0008188593,0.0002061353,0.0004715359,0.0003768617,0.0006116527,0.001818603],"category_scores_gemma":[0.0002864595,0.0003220482,0.0004228239,0.0003512301,0.0002538007,0.0002790263,0.0002437766,0.0008211613,0.000620663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005078026,"about_ca_system_score_gemma":0.0002922592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002793282,"about_ca_topic_score_gemma":0.001975443,"domain_scores_codex":[0.999731,0.00003443254,0.00001778767,0.00007714447,0.00009956671,0.00004017518],"domain_scores_gemma":[0.9998577,0.00003252876,0.00003836991,0.00001683388,0.00002513688,0.00002943085],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002707344,0.0001376049,0.001903377,0.0001439173,0.00002511821,0.000130153,0.00006778548,0.003226351,0.9828909,0.0009236825,0.0006991982,0.009581149],"study_design_scores_gemma":[0.00009160754,0.0009619621,0.009567827,0.00003611315,0.0001495959,0.001054129,0.00007393771,0.06971353,0.8984171,0.0006641106,0.01919614,0.00007398716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8880635,0.004589399,0.09349365,0.0003972398,0.0001746536,0.0002940504,0.004989762,0.001491802,0.006505829],"genre_scores_gemma":[0.9543295,0.001633961,0.03608671,0.0001134472,0.00001738288,0.0002050468,0.003880019,0.000104577,0.003629337],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002793282,"threshold_uncertainty_score":0.006083786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01482751470210941,"score_gpt":0.288236127921865,"score_spread":0.2734086132197556,"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."}}