{"id":"W4403152806","doi":"10.1182/bloodadvances.2024013756","title":"Novel machine learning technique further clarifies unrelated donor selection to optimize transplantation outcomes","year":2024,"lang":"en","type":"article","venue":"Blood Advances","topic":"Organ Transplantation Techniques and Outcomes","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Cancer Moonshot; National Institute of Environmental Health Sciences; National Institute of Allergy and Infectious Diseases; Office of Naval Research; Legend Biotech; Pharmacyclics; Takeda Oncology; Health Resources and Services Administration; National Institutes of Health; MorphoSys; Seagen; Astellas Pharma; Adaptive Biotechnologies; Pfizer; Incyte; Kiadis Pharma; bluebird bio; Medac; Jazz Pharmaceuticals; BeiGene; HistoGenetics; Atara Biotherapeutics; CareDx; Actinium Pharmaceuticals; National Cancer Institute; Gilead Sciences; Moonshot Research and Development Program; Sanofi; GlaxoSmithKline; CSL Behring; Bristol-Myers Squibb; AstraZeneca; Gateway for Cancer Research; Swedish Orphan Biovitrum; Omeros Corporation; Vertex Pharmaceuticals; Alexion Pharmaceuticals; Mallinckrodt Pharmaceuticals; Astellas Pharma US; Amgen; National Heart, Lung, and Blood Institute; Novartis Pharmaceuticals Corporation","keywords":"Selection (genetic algorithm); Transplantation; Computer science; Computational biology; Machine learning; Artificial intelligence; Medicine; Biology; Internal medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002538897,0.0004549055,0.0005566401,0.0007785829,0.0001961101,0.0005861499,0.0004720971,0.0004477169,0.001677475],"category_scores_gemma":[0.005580277,0.0001120695,0.0004703435,0.0005325195,0.0001236037,0.0005138483,0.0004477164,0.000647822,0.0005648925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002443735,"about_ca_system_score_gemma":0.0005383029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000612877,"about_ca_topic_score_gemma":0.0009137057,"domain_scores_codex":[0.9992311,0.0003918667,0.00006168189,0.0001259069,0.0001426063,0.00004683949],"domain_scores_gemma":[0.9983227,0.000862804,0.0003103358,0.0001758965,0.0002499647,0.000078367],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001119207,0.0007513493,0.3543153,0.0002012453,0.0003622218,0.0002902853,0.000128923,0.1483826,0.01088673,0.00235895,0.003215647,0.4779875],"study_design_scores_gemma":[0.00006378953,0.0004071555,0.04786438,0.00003057122,0.0001031231,0.0004756147,0.00002928211,0.940228,0.005777825,0.002738413,0.002249523,0.0000323183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6627015,0.000859803,0.3322313,0.0005391965,0.0001220832,0.00009732546,0.0006401041,0.0005846632,0.002223982],"genre_scores_gemma":[0.9511889,0.00009703581,0.04751608,0.00006101196,0.00006883442,0.00003754821,0.0004082443,0.00003603852,0.000586388],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002538897,"threshold_uncertainty_score":0.01342708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009809484060171627,"score_gpt":0.2815315142204878,"score_spread":0.2717220301603162,"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."}}