{"id":"W4312118687","doi":"10.1016/j.clml.2022.12.013","title":"A Comprehensive Multidisciplinary Diagnostic Algorithm for the Early and Efficient Detection of Amyloidosis","year":2022,"lang":"en","type":"article","venue":"Clinical Lymphoma Myeloma & Leukemia","topic":"Amyloidosis: Diagnosis, Treatment, Outcomes","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; Ottawa Hospital; University of Ottawa; Université de Montréal; Hôpital Maisonneuve-Rosemont; University Health Network; University of Toronto; University of Calgary; Centre Hospitalier de l’Université de Montréal; Alberta Cancer Foundation","funders":"","keywords":"Amyloidosis; Medicine; AL amyloidosis; Algorithm; Disease; Amyloid fibril; Amyloid (mycology); Multidisciplinary approach; Intensive care medicine; Pathology; Computer science; Immunoglobulin light chain; Amyloid β; Immunology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004075271,0.0002798783,0.0004245982,0.00005599358,0.0005409943,0.00002364193,0.0003486892,0.0001760574,0.00001234377],"category_scores_gemma":[0.0005772922,0.0002267255,0.0004082998,0.0001726979,0.0004209689,0.000004689196,0.0007340563,0.000133191,0.000005426063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001801395,"about_ca_system_score_gemma":0.000194954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001166493,"about_ca_topic_score_gemma":0.00001214582,"domain_scores_codex":[0.9977829,0.0002685891,0.0006801494,0.0006683312,0.0002498148,0.0003502074],"domain_scores_gemma":[0.9961209,0.002678148,0.0003457982,0.0005824267,0.0001363611,0.0001363762],"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":[0.0004404128,0.0004857221,0.03125999,0.00003548059,0.0004914794,0.00001468387,0.0001508672,0.0006761918,0.01724589,0.00002648685,0.0003562427,0.9488165],"study_design_scores_gemma":[0.003394374,0.002025141,0.95998,0.00001519997,0.0002487797,0.00004126227,0.0003776191,0.006663215,0.01858036,0.00004667711,0.008338881,0.0002884805],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9911398,0.00347828,0.00333041,0.0002153622,0.0005661747,0.001002599,0.000222778,0.00002719049,0.00001740003],"genre_scores_gemma":[0.9962536,0.0006976554,0.001282858,0.0003122156,0.0002348992,0.000989192,0.00007147936,0.00004994019,0.0001081513],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9485281,"threshold_uncertainty_score":0.9245598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01975615960631867,"score_gpt":0.3001207432873258,"score_spread":0.2803645836810071,"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."}}