{"id":"W4406981628","doi":"10.1016/j.jocmr.2024.101445","title":"Radiomics provides incremental value in distinguishing between ATTR and AL on cardiac magnetic resonance imaging","year":2025,"lang":"en","type":"article","venue":"Journal of Cardiovascular Magnetic Resonance","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Angiology; Radiomics; Medicine; Magnetic resonance imaging; Cardiac magnetic resonance; Cardiac magnetic resonance imaging; Value (mathematics); Cardiac imaging; Radiology; Cardiology; Computer science; Machine learning","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":[0.0029839,0.001041191,0.001036482,0.002021897,0.0002304415,0.002125342,0.0009743106,0.001612961,0.002082491],"category_scores_gemma":[0.01425654,0.0003620892,0.0005800406,0.000657487,0.0004809485,0.002162826,0.0007102709,0.001230161,0.001178916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002650988,"about_ca_system_score_gemma":0.0003823458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001048507,"about_ca_topic_score_gemma":0.002287172,"domain_scores_codex":[0.9990379,0.0003398664,0.00009200441,0.0001681118,0.0002731494,0.00008887972],"domain_scores_gemma":[0.9896961,0.006591398,0.0007769917,0.000735615,0.001625829,0.000574066],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004872363,0.0004571906,0.7664481,0.0002036301,0.0005196748,0.001124764,0.0001201992,0.003855329,0.01704289,0.0005855808,0.005031294,0.199739],"study_design_scores_gemma":[0.0005159586,0.003314029,0.6902036,0.0002771899,0.002200095,0.01092282,0.0007994198,0.2315524,0.03651934,0.01060737,0.0128228,0.0002649002],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9690232,0.005019895,0.01458134,0.001370655,0.0005148349,0.00009298221,0.001477121,0.0009036203,0.00701638],"genre_scores_gemma":[0.9874233,0.0009314996,0.00862496,0.0003629554,0.0006495221,0.00002305467,0.001024595,0.00006168655,0.0008985314],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0029839,"threshold_uncertainty_score":0.01578057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007642225338531649,"score_gpt":0.2584787628022216,"score_spread":0.2508365374636899,"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."}}