{"id":"W2090366510","doi":"10.1002/jmri.20697","title":"Characterizing coronary motion and its effect on MR coronary angiography—Initial experience","year":2006,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre","funders":"","keywords":"Coronary arteries; Artery; Cardiac cycle; Temporal resolution; Image resolution; Displacement (psychology); Breathing; Medicine; Biomedical engineering; Nuclear medicine; Computer science; Artificial intelligence; Cardiology; Physics; Anatomy; Optics","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":[0.002879584,0.000683459,0.0003655222,0.0003886288,0.0002947204,0.000378146,0.0005402111,0.000864569,0.001243972],"category_scores_gemma":[0.006504211,0.0002249082,0.0002649485,0.0002106915,0.0004866614,0.0005165758,0.0004764811,0.0004585074,0.0003560939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003112093,"about_ca_system_score_gemma":0.0002165928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007533007,"about_ca_topic_score_gemma":0.0009659423,"domain_scores_codex":[0.9988934,0.0005816735,0.00007053788,0.000152214,0.0001840866,0.0001180855],"domain_scores_gemma":[0.9979531,0.001243109,0.0001144259,0.0002013503,0.0002133509,0.0002746804],"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":[0.01104001,0.02073519,0.1813335,0.0007512192,0.0005217216,0.006287131,0.007272353,0.02243808,0.2217101,0.000524762,0.002112696,0.5252733],"study_design_scores_gemma":[0.001322204,0.1845209,0.4514355,0.0002436767,0.0007147169,0.02787128,0.002335468,0.03076389,0.2817309,0.001246651,0.01740614,0.0004086471],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9933482,0.0009248987,0.004617465,0.00007943048,0.000006086003,0.0000764958,0.00003329467,0.00004710833,0.0008670615],"genre_scores_gemma":[0.9906653,0.00091927,0.007420627,0.0001258525,0.00003914293,0.00004894317,0.00009621333,0.00003041444,0.0006542198],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002879584,"threshold_uncertainty_score":0.01522893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01051768810348658,"score_gpt":0.2955227174937105,"score_spread":0.2850050293902239,"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."}}