{"id":"W2575694335","doi":"10.3390/metabo7010003","title":"Distinguishing Benign from Malignant Pancreatic and Periampullary Lesions Using Combined Use of 1H-NMR Spectroscopy and Gas Chromatography–Mass Spectrometry","year":2017,"lang":"en","type":"article","venue":"Metabolites","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Alberta Innovates; Alberta Cancer Foundation","keywords":"Metabolomics; Pancreatic cancer; Glutamine; Gas chromatography–mass spectrometry; Periampullary cancer; Nuclear magnetic resonance spectroscopy; In vivo magnetic resonance spectroscopy; Adenocarcinoma; Chemistry; Receiver operating characteristic; Mass spectrometry; Internal medicine; Medicine; Gastroenterology; Magnetic resonance imaging; Cancer; Radiology; Chromatography; Biochemistry; Amino acid","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":[0.003018179,0.0009546267,0.001059478,0.001551903,0.0003194617,0.001065929,0.0002624897,0.0004811615,0.0005946692],"category_scores_gemma":[0.005414827,0.0002634779,0.0009797814,0.0008952069,0.0004177672,0.0005553108,0.0006574261,0.0006179264,0.0002614322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003602868,"about_ca_system_score_gemma":0.0005439485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002492202,"about_ca_topic_score_gemma":0.004288609,"domain_scores_codex":[0.9988445,0.0005183564,0.000122317,0.0002635281,0.0001616116,0.00008967803],"domain_scores_gemma":[0.9979727,0.00126297,0.0002609737,0.000210098,0.0001807329,0.0001125845],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003066344,0.0002648004,0.8517084,0.0002106418,0.000947352,0.0003968909,0.0002108947,0.01832709,0.03703994,0.0001921383,0.0005902641,0.08704516],"study_design_scores_gemma":[0.000132157,0.001167973,0.7501373,0.00004809965,0.0006918128,0.001585188,0.000292728,0.221321,0.02078198,0.00215021,0.001609068,0.00008237278],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9867592,0.001188893,0.01054305,0.0001488764,0.00001588461,0.0000631451,0.0006002507,0.0001107605,0.0005698731],"genre_scores_gemma":[0.9923172,0.0002424491,0.006573302,0.00003407666,0.00001439445,0.00002198403,0.0006841419,0.0000108503,0.0001015863],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003018179,"threshold_uncertainty_score":0.01596189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02452949219670342,"score_gpt":0.2639301797408993,"score_spread":0.2394006875441959,"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."}}