{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002140175,0.001158619,0.001163989,0.005477869,0.001288317,0.002435042,0.001529786,0.00142931,0.003713239],"category_scores_gemma":[0.004738199,0.0004566233,0.001263366,0.001610256,0.0002949965,0.001876851,0.002176353,0.00213717,0.001165376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00203086,"about_ca_system_score_gemma":0.004315606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004795975,"about_ca_topic_score_gemma":0.007258894,"domain_scores_codex":[0.9985275,0.0004248995,0.0002871501,0.0002155217,0.0003547698,0.0001901719],"domain_scores_gemma":[0.9978206,0.0003826514,0.0002504872,0.00007963637,0.001099005,0.0003676787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006614153,0.001315585,0.3801492,0.0003788726,0.00032119,0.005535879,0.000491889,0.0127898,0.003901478,0.004888786,0.08347631,0.5060896],"study_design_scores_gemma":[0.0005965562,0.001622839,0.7016905,0.003689406,0.00107505,0.04601261,0.002559518,0.1260352,0.007983376,0.03213371,0.07624205,0.0003591251],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3841091,0.04061292,0.3989535,0.0735333,0.002189914,0.00729449,0.006216776,0.005963448,0.08112659],"genre_scores_gemma":[0.6560684,0.007366842,0.3158578,0.005049716,0.001133797,0.002141631,0.005848334,0.0002012968,0.006332114],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005477869,"threshold_uncertainty_score":0.01473504,"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."}}