{"id":"W4416275866","doi":"10.1016/j.jocmr.2025.101988","title":"Quantitative myocardial blood flow and perfusion reserve with exercise cardiovascular magnetic resonance","year":2025,"lang":"en","type":"article","venue":"Journal of Cardiovascular Magnetic Resonance","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Siemens (Canada)","funders":"National Heart, Lung, and Blood Institute; National Institutes of Health; Deutsche Forschungsgemeinschaft; American Heart Association","keywords":"Angiology; Perfusion; Blood flow; Magnetic resonance imaging; Cardiac magnetic resonance; Intensity (physics); Perfusion scanning","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":[0.001875934,0.0004691547,0.0003389709,0.0007934081,0.0001018167,0.0003592188,0.0003673946,0.0003336864,0.002266928],"category_scores_gemma":[0.002258104,0.000147708,0.0001882813,0.0003826941,0.0003494333,0.0003574665,0.0002973946,0.0002830485,0.0003551109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008805873,"about_ca_system_score_gemma":0.00008439121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001438717,"about_ca_topic_score_gemma":0.0002411029,"domain_scores_codex":[0.9994676,0.0002585856,0.00004370782,0.00009595737,0.0001010124,0.00003314649],"domain_scores_gemma":[0.9991679,0.0003622026,0.0002481433,0.00007701688,0.00009158129,0.00005319343],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.006666812,0.000734617,0.5860958,0.0008162419,0.0003827065,0.00103583,0.0004199893,0.003272782,0.2106018,0.0005250554,0.000814772,0.1886336],"study_design_scores_gemma":[0.0001363448,0.003406568,0.9487709,0.00006357772,0.000197107,0.006844951,0.00009814941,0.01132353,0.02703344,0.0006801422,0.001404985,0.0000402683],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9676558,0.002795688,0.02540515,0.00007744088,0.00001767237,0.0002064653,0.0005588983,0.0001541115,0.003128811],"genre_scores_gemma":[0.9866917,0.0003127027,0.01217587,0.00002757667,0.00003510435,0.0001498494,0.000232795,0.00001707544,0.0003574022],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002266928,"threshold_uncertainty_score":0.009921014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007798813492408261,"score_gpt":0.2315863824853931,"score_spread":0.2237875689929848,"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."}}