{"id":"W78156908","doi":"10.1007/2789_2008_089","title":"Using Metabolomics to Monitor Anticancer Drugs","year":2008,"lang":"en","type":"review","venue":"Ernst Schering Research Foundation workshop/Ernst Schering Foundation Symposium proceedings","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institute of Cancer Research","funders":"","keywords":"In vivo; Context (archaeology); Drug; Pharmacology; Metabolome; Pharmacodynamics; Drug action; Drug development; Cancer; Ex vivo; Medicine; Chemistry; Metabolomics; Bioinformatics; Biology; Pharmacokinetics; Internal medicine; Biotechnology","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":[0.0006550992,0.001499069,0.001375084,0.002097287,0.0001841702,0.0009157678,0.0008117739,0.001410789,0.002248083],"category_scores_gemma":[0.0006230833,0.0003481924,0.0005586956,0.001766663,0.0004518296,0.00103881,0.0006906807,0.001349668,0.002999887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006106566,"about_ca_system_score_gemma":0.0004237353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008197172,"about_ca_topic_score_gemma":0.001588847,"domain_scores_codex":[0.9997521,0.00003446389,0.00001541611,0.00005227314,0.0001270485,0.00001871287],"domain_scores_gemma":[0.9997705,0.00009813216,0.00002659171,0.00001135308,0.00007716215,0.00001640378],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000139763,0.00008765022,0.0001897726,0.003046281,0.00009245206,0.0001684218,0.00001326864,0.0002493058,0.01706631,0.001628641,0.03095145,0.9463667],"study_design_scores_gemma":[0.00006566231,0.0002305524,0.001971991,0.0008923736,0.0002012671,0.001672743,0.00004231553,0.0005983935,0.02343052,0.002854152,0.967988,0.00005201911],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0003194203,0.9937749,0.001888934,0.000881682,0.0007366856,0.00001348452,0.00005777044,0.00005915702,0.002267959],"genre_scores_gemma":[0.002658112,0.9895402,0.001959713,0.0008052253,0.0006581289,0.00003122796,0.00009539802,0.000007962153,0.004244032],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002248083,"threshold_uncertainty_score":0.007520556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1354504135983668,"score_gpt":0.4303036436588048,"score_spread":0.294853230060438,"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."}}