{"id":"W4416816832","doi":"10.1111/ctr.70402","title":"One Size Doesn't Fit All: A Review of International Deceased Donor Kidney Allocation Algorithms","year":2025,"lang":"en","type":"article","venue":"Clinical Transplantation","topic":"Organ Donation and Transplantation","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University Health Network","funders":"","keywords":"Transparency (behavior); Kidney transplantation; MEDLINE; Location-allocation; Cost allocation","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007508266,0.0001509168,0.0004375609,0.0001402611,0.000037181,0.00001280635,0.000137326,0.0001632425,0.0006664586],"category_scores_gemma":[0.0007567061,0.0001440687,0.0002361754,0.0003309721,0.00009648356,0.0001507618,0.000006048665,0.0002238434,0.00004612458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004779325,"about_ca_system_score_gemma":0.0002787247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001662694,"about_ca_topic_score_gemma":0.00001019485,"domain_scores_codex":[0.9978076,0.0001374911,0.001166083,0.000340461,0.0004090142,0.0001393527],"domain_scores_gemma":[0.9983354,0.0006312804,0.000238809,0.0002292191,0.0004044681,0.0001608299],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.01466337,0.01329633,0.32412,0.1550644,0.007375567,0.0002249498,0.001829542,0.00004997427,0.04629115,0.02815544,0.02955539,0.3793739],"study_design_scores_gemma":[0.01802529,0.0005413469,0.8963344,0.03440556,0.004140445,0.00004566467,0.00004623884,0.001241495,0.02143324,0.001414015,0.02189004,0.0004823458],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1986347,0.01325545,0.321227,0.3232301,0.01176935,0.01136781,0.002305308,0.001381921,0.1168283],"genre_scores_gemma":[0.6833768,0.2292541,0.02285311,0.05700361,0.0004519407,0.0001119945,0.004936215,0.00004544153,0.001966688],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5722144,"threshold_uncertainty_score":0.7297254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05923412551727582,"score_gpt":0.4061956091185454,"score_spread":0.3469614836012695,"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."}}