{"id":"W3084380855","doi":"10.1101/2020.09.08.287573","title":"KuLGaP: A Selective Measure for Assessing Therapy Response in Patient-Derived Xenografts","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research; Princess Margaret Cancer Centre; Canadian Institute for Advanced Research; Vector Institute; Hospital for Sick Children; Toronto General Hospital; University Health Network; University of Toronto; Université de Montréal; Group for Research in Decision Analysis; Université du Québec à Montréal","funders":"Canadian Institutes of Health Research; Concordia University; Canadian Cancer Society Research Institute; Terry Fox Foundation; Ontario Institute for Cancer Research; Terry Fox Research Institute; California HIV/AIDS Research Program; Canadian Institute for Advanced Research; Princess Margaret Cancer Foundation; Stand Up To Cancer; Entertainment Industry Foundation; Government of Ontario; American Association for Cancer Research","keywords":"Measure (data warehouse); Task (project management); Translation (biology); Computer science; Variation (astronomy); Clinical Practice; Medicine; Artificial intelligence; Oncology; Machine learning; Data mining; Biology; Family medicine","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":[0.003568962,0.0006659339,0.0007013873,0.001636691,0.0003483548,0.001359333,0.0008169031,0.001044677,0.001348141],"category_scores_gemma":[0.009244146,0.0002485638,0.0004195219,0.0008954519,0.0008455512,0.0007495958,0.001257644,0.001170584,0.0005990135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006280342,"about_ca_system_score_gemma":0.0003600926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00029966,"about_ca_topic_score_gemma":0.0003634211,"domain_scores_codex":[0.9980142,0.0008209995,0.00009949673,0.0004140414,0.000529435,0.0001217881],"domain_scores_gemma":[0.9958567,0.002450668,0.000631717,0.0005268482,0.0003472226,0.0001868008],"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":[0.002100304,0.0003974673,0.0772633,0.00110515,0.0007204331,0.0003048499,0.000337673,0.06826957,0.6412829,0.01623624,0.007534002,0.1844482],"study_design_scores_gemma":[0.0001076585,0.001346354,0.08906279,0.00009950255,0.0002679912,0.001303161,0.000356435,0.3396653,0.5354274,0.0207575,0.01141447,0.0001915284],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4474795,0.001711292,0.5373442,0.0006582431,0.0001486602,0.0001745399,0.005850788,0.003634459,0.002998164],"genre_scores_gemma":[0.883962,0.0002707323,0.1106621,0.0002980386,0.00006302894,0.0003778128,0.002997618,0.0004735719,0.0008950165],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003568962,"threshold_uncertainty_score":0.0188747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02419053886704689,"score_gpt":0.2404200375951865,"score_spread":0.2162294987281396,"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."}}