{"id":"W2982488323","doi":"10.1016/j.bbmt.2019.10.016","title":"Killer Immunoglobulin-Like Receptor-Ligand Interactions Predict Clinical Outcomes following Unrelated Donor Transplantations","year":2019,"lang":"en","type":"article","venue":"Biology of Blood and Marrow Transplantation","topic":"Immune Cell Function and Interaction","field":"Immunology and Microbiology","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto","funders":"National Cancer Institute","keywords":"Medicine; Hazard ratio; Receptor; Transplantation; Immunology; Human leukocyte antigen; Hematopoietic cell; Internal medicine; Hematopoietic stem cell transplantation; Oncology; Haematopoiesis; Biology; Confidence interval; Antigen; Genetics; Stem cell","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":[0.0007157823,0.0002477444,0.0003454562,0.0004085918,0.0003979547,0.001089988,0.000375031,0.0006434851,0.001801384],"category_scores_gemma":[0.003199828,0.0001028857,0.000275276,0.0004182363,0.0005053699,0.0004511102,0.0005188247,0.001004071,0.0004261876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003387964,"about_ca_system_score_gemma":0.0003032248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004668714,"about_ca_topic_score_gemma":0.0006299086,"domain_scores_codex":[0.9995362,0.0001507949,0.00004595969,0.00006501512,0.00006280148,0.0001392195],"domain_scores_gemma":[0.9975062,0.0007707982,0.0008775278,0.0001319169,0.0001448384,0.0005687379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006626906,0.00009681717,0.9944757,0.000007732992,0.00003667158,0.0002215778,0.0000382444,0.00009996977,0.0009786347,0.00005812414,0.0001144824,0.003209374],"study_design_scores_gemma":[0.00001394914,0.0004077027,0.9966396,0.000007321675,0.00005751753,0.0009184744,0.0002144833,0.0005882806,0.0006036331,0.000223307,0.0003173769,0.000008467137],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988785,0.0002499568,0.00008839922,0.00005310267,0.00001630046,0.000003145364,0.00004732653,0.000003578294,0.0006595978],"genre_scores_gemma":[0.9996258,0.00005660702,0.00002337174,0.00002301032,0.00002114657,0.000002475109,0.00007982992,0.000001070944,0.0001666641],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001801384,"threshold_uncertainty_score":0.006026208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01260362363285795,"score_gpt":0.2685123496999358,"score_spread":0.2559087260670779,"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."}}