{"id":"W4406981400","doi":"10.1016/j.jocmr.2024.101592","title":"Clinical feasibility and validation of the peripheral pulse gating for quantitative evaluation of myocardial parametric mapping in cardiac magnetic resonance imaging","year":2025,"lang":"en","type":"article","venue":"Journal of Cardiovascular Magnetic Resonance","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University Health Centre","funders":"","keywords":"Angiology; Medicine; Cardiac magnetic resonance; Magnetic resonance imaging; Cardiac magnetic resonance imaging; Gating; Pulse (music); Parametric statistics; Cardiac imaging; Cardiology; Radiology; Internal medicine; Nuclear magnetic resonance; Nuclear medicine; Medical physics; Computer science","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":[0.007079408,0.0005182183,0.000415016,0.000455559,0.0002668333,0.001548787,0.0005913182,0.001165569,0.001004823],"category_scores_gemma":[0.01428982,0.0004545323,0.0001773769,0.0003240862,0.001036673,0.0008219676,0.0005235301,0.000893937,0.0003964915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002011234,"about_ca_system_score_gemma":0.0007139634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004407581,"about_ca_topic_score_gemma":0.0004580629,"domain_scores_codex":[0.997554,0.001656192,0.0000807571,0.000306704,0.0002979617,0.0001043911],"domain_scores_gemma":[0.9946617,0.00350634,0.0003101727,0.0006424183,0.0006874983,0.000191861],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.01466634,0.0009784148,0.1150947,0.0006814534,0.0001970183,0.001394484,0.00100973,0.002570204,0.5878484,0.002559603,0.001547684,0.2714519],"study_design_scores_gemma":[0.001888227,0.02404464,0.4617498,0.0004135903,0.001213805,0.02439573,0.0006571583,0.108137,0.3590074,0.00379562,0.01443288,0.0002642763],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8436282,0.006122781,0.1443795,0.000499912,0.0002038342,0.0004382154,0.0002537377,0.0004934533,0.003980254],"genre_scores_gemma":[0.9726017,0.0005683573,0.02610764,0.000148717,0.00006607962,0.00009490171,0.00008187299,0.0001035552,0.0002272786],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007079408,"threshold_uncertainty_score":0.03743994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04724077258310677,"score_gpt":0.3526066176108534,"score_spread":0.3053658450277466,"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."}}