{"id":"W4383426621","doi":"10.7202/1101126ar","title":"The Rule of Rescue in the Era of Precision Medicine, HLA Eplet Matching, and Organ Allocation","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Bioethics","topic":"Renal Transplantation Outcomes and Treatments","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; McGill University Health Centre; University of Alberta","funders":"Amgen Canada; Amgen","keywords":"Matching (statistics); Resource allocation; Organ donation; Allocator; Computer science; Complement (music); Intensive care medicine; Risk analysis (engineering); Medicine; Transplantation; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.001219722,0.00005429994,0.0001617876,0.0001476377,0.00007051678,0.000008034262,0.0001016417,0.00006056669,0.00001115417],"category_scores_gemma":[0.0002560152,0.00002652602,0.00003448372,0.0002854569,0.0002435645,0.00003239972,0.000002782701,0.0003071599,0.000001080635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002674399,"about_ca_system_score_gemma":0.000441486,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01061232,"about_ca_topic_score_gemma":0.03187445,"domain_scores_codex":[0.9991593,0.00008541738,0.000359893,0.000047418,0.0002507591,0.00009724761],"domain_scores_gemma":[0.9989922,0.0004711162,0.0001501546,0.0001137009,0.0001612162,0.00011159],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00116489,0.0001702963,0.6140174,0.001864528,0.0009801055,0.001708296,0.2297322,0.0005435242,0.009061032,0.04726545,0.004019918,0.08947234],"study_design_scores_gemma":[0.002978299,0.0008368677,0.9700168,0.002977489,0.0002092339,0.0004510185,0.01071631,0.0000773577,0.002121985,0.006918951,0.002615453,0.00008018708],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9575872,0.0008677124,0.00004798504,0.04093795,0.0001426711,0.0001348932,0.00001577772,0.000001428267,0.0002643795],"genre_scores_gemma":[0.9974033,0.002020742,0.0001298007,0.0003223792,0.00003750627,6.519272e-7,0.000008738047,0.000005687299,0.0000712011],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3559995,"threshold_uncertainty_score":0.9959761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05216998741069438,"score_gpt":0.3437194853002045,"score_spread":0.2915494978895101,"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."}}