{"id":"W4399989763","doi":"10.1109/access.2024.3418146","title":"Improving 1-Year Mortality Prediction After Pediatric Heart Transplantation Using Hypothetical Donor-Recipient Matches","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Organ Transplantation Techniques and Outcomes","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Libin Cardiovascular Institute of Alberta; University of Calgary","funders":"Health Resources and Services Administration; Vlaamse regering; U.S. Department of Health and Human Services","keywords":"Transplantation; Heart transplantation; Medicine; Computer science; Internal medicine","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":[0.002402294,0.0003652129,0.0003584823,0.0005290065,0.0002393094,0.0004970062,0.0004606834,0.0004424498,0.0007184336],"category_scores_gemma":[0.007121459,0.000113057,0.0003617281,0.0004180628,0.0001869199,0.0004283497,0.0005622687,0.0004752335,0.0001763919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00036711,"about_ca_system_score_gemma":0.0005177559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002521284,"about_ca_topic_score_gemma":0.00258538,"domain_scores_codex":[0.9993789,0.0003433021,0.00003409775,0.0001310548,0.00006247735,0.00005024569],"domain_scores_gemma":[0.9964601,0.002183049,0.000483141,0.0003864948,0.0003078306,0.0001794478],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001204242,0.0003916504,0.5664878,0.00004786797,0.0001281806,0.0002605851,0.0001526349,0.3687065,0.001548417,0.001018026,0.001815867,0.05823815],"study_design_scores_gemma":[0.00002372922,0.0003591792,0.05373871,0.00001706944,0.00003328256,0.0001577047,0.0001168395,0.94117,0.002363208,0.001211027,0.0007919533,0.00001727516],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.986645,0.00008809268,0.01204726,0.0001232381,0.00002159603,0.00001985622,0.0006728603,0.00005633056,0.0003257212],"genre_scores_gemma":[0.9924115,0.00005652508,0.005777833,0.00002040243,0.00001266353,0.0000134636,0.001562325,0.000004739308,0.0001405042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002521284,"threshold_uncertainty_score":0.01270467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03911881176964876,"score_gpt":0.3387839996218803,"score_spread":0.2996651878522315,"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."}}