{"id":"W1516408604","doi":"10.1111/j.1617-0830.2007.00108.x","title":"Clinical applications of intravascular magnetic resonance contrast agents","year":2008,"lang":"en","type":"article","venue":"Imaging Decisions MRI","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Contrast (vision); Magnetic resonance imaging; Radiology; Image contrast; Image enhancement; Nuclear medicine; Artificial intelligence; Computer science; Image (mathematics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001295305,0.0006333704,0.0003063836,0.001075893,0.0003028284,0.001171358,0.0005296847,0.001127586,0.0052387],"category_scores_gemma":[0.00214117,0.0001671201,0.0002610121,0.0004830039,0.0006166738,0.000518686,0.0005638457,0.001045534,0.002531516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004458651,"about_ca_system_score_gemma":0.0004690044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003809384,"about_ca_topic_score_gemma":0.0003011407,"domain_scores_codex":[0.9994288,0.0002639374,0.0000336723,0.00007232456,0.0001518602,0.00004922723],"domain_scores_gemma":[0.9988005,0.000534097,0.0001004419,0.00007053959,0.0003631135,0.0001311652],"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":[0.0007308279,0.0003008436,0.004866263,0.001692267,0.00005562012,0.006560227,0.0002184046,0.0007568459,0.07631245,0.01227757,0.02849577,0.8677329],"study_design_scores_gemma":[0.0003491585,0.003118696,0.01016064,0.001963004,0.0002131127,0.05891718,0.0002441555,0.003556485,0.08839314,0.01670631,0.8162978,0.00008037356],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.05074329,0.7771702,0.02754007,0.01699385,0.00328372,0.0002503432,0.0001241623,0.0006682335,0.1232262],"genre_scores_gemma":[0.7624258,0.1782573,0.02862346,0.008788346,0.00728716,0.000220207,0.0002254193,0.0001101673,0.01406216],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0052387,"threshold_uncertainty_score":0.01752526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03817105893866971,"score_gpt":0.3811216603037091,"score_spread":0.3429506013650394,"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."}}