{"id":"W4396230545","doi":"10.3390/metabo14050254","title":"Harnessing Metabolites as Serum Biomarkers for Liver Graft Pathology Prediction Using Machine Learning","year":2024,"lang":"en","type":"article","venue":"Metabolites","topic":"Liver Disease Diagnosis and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network","funders":"American Society of Transplantation","keywords":"Pathology; Medicine; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003390306,0.0002926398,0.0004786708,0.0003283886,0.000228091,0.0001458196,0.00006241071,0.0001140599,0.0002150379],"category_scores_gemma":[0.0002123053,0.0002268701,0.0003707477,0.0003470466,0.0000864983,0.0002757751,0.00005052712,0.0001668284,0.00004728377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007361358,"about_ca_system_score_gemma":0.0001313399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001276683,"about_ca_topic_score_gemma":0.000003310886,"domain_scores_codex":[0.9983422,0.000126721,0.0003275393,0.0005510399,0.0002355313,0.0004169673],"domain_scores_gemma":[0.9991367,0.0002516309,0.0000698219,0.000217604,0.0001259282,0.0001982681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000514979,0.001018767,0.7484263,0.001819588,0.004786993,0.001161292,0.001668656,0.0000842865,0.1238023,0.009327452,0.0004125925,0.1069768],"study_design_scores_gemma":[0.00540176,0.0007809677,0.4359513,0.001123424,0.01663035,0.0008533375,0.000702675,0.1189044,0.1814529,0.001805006,0.2355006,0.000893341],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8215716,0.1752385,0.0005606213,0.0004212057,0.0006808568,0.0006738539,0.0002761532,0.000332373,0.0002447946],"genre_scores_gemma":[0.9906336,0.002170725,0.005162728,0.0002633061,0.0005039534,0.0001655511,0.0004079606,0.00007841209,0.0006137812],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.312475,"threshold_uncertainty_score":0.9251493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02645259125315863,"score_gpt":0.29626886886453,"score_spread":0.2698162776113714,"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."}}