{"id":"W2104088734","doi":"10.1016/j.healun.2007.11.546","title":"532: Donor Scoring Does Not Predict Early Outcome","year":2008,"lang":"en","type":"article","venue":"The Journal of Heart and Lung Transplantation","topic":"Transplantation: Methods and Outcomes","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Outcome (game theory); Medicine; Lung transplantation; Selection (genetic algorithm); Scoring system; Transplantation; Intensive care medicine; Internal medicine; Computer science; Artificial intelligence; Mathematics","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":[0.004732464,0.0006268002,0.000826036,0.0006629422,0.0004572808,0.001508487,0.000868835,0.0008602,0.008784771],"category_scores_gemma":[0.009092788,0.0001879405,0.000548011,0.0008686077,0.0005557803,0.001132339,0.0008201826,0.000971361,0.002491797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002580212,"about_ca_system_score_gemma":0.0004276249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003184097,"about_ca_topic_score_gemma":0.0007807222,"domain_scores_codex":[0.9983826,0.0007686923,0.0001732509,0.0001860704,0.0002551268,0.0002342015],"domain_scores_gemma":[0.9915326,0.002853524,0.002630401,0.001002423,0.0005421378,0.001438773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001531323,0.0001266948,0.9786951,0.00004375479,0.0001463575,0.0001652333,0.00006401211,0.0001571962,0.001098561,0.0002419365,0.001203706,0.01652611],"study_design_scores_gemma":[0.0001579254,0.001236983,0.9847931,0.0001288403,0.0004039067,0.003381602,0.0002964187,0.003189666,0.001513911,0.002720821,0.002128625,0.00004834115],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931165,0.0008494825,0.0010936,0.000445301,0.0002112418,0.0000127801,0.0004093596,0.00002298743,0.00383881],"genre_scores_gemma":[0.9986359,0.00008453443,0.0002125676,0.0001272052,0.00009110476,0.000006989012,0.0002595986,0.0000160791,0.0005660577],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008784771,"threshold_uncertainty_score":0.02938801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03498640093658979,"score_gpt":0.3174674377634784,"score_spread":0.2824810368268886,"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."}}