{"id":"W2067932028","doi":"10.1186/1532-429x-10-s1-a267","title":"1142 A new approach towards improved visualization of myocardial edema using T2-weighted imaging","year":2008,"lang":"en","type":"article","venue":"Journal of Cardiovascular Magnetic Resonance","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Siemens (Canada); Libin Cardiovascular Institute of Alberta; University of Calgary","funders":"","keywords":"Visualization; Edema; Artificial intelligence; Medicine; Computer science; Internal medicine","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":[0.001581749,0.0008341038,0.0004760008,0.002979578,0.0006375976,0.00261546,0.001251597,0.002027879,0.006971278],"category_scores_gemma":[0.001751764,0.0007896128,0.0006898498,0.001164891,0.000951415,0.003242209,0.001822179,0.00278583,0.002694428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003000534,"about_ca_system_score_gemma":0.0006582001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007646165,"about_ca_topic_score_gemma":0.001500092,"domain_scores_codex":[0.9994374,0.000154988,0.00004212194,0.0001153205,0.0002053648,0.00004470476],"domain_scores_gemma":[0.9994047,0.0001444024,0.00004265732,0.0001145262,0.0002222706,0.00007139425],"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":[0.0003878031,0.000174605,0.001542843,0.0007896274,0.0001501068,0.0008695014,0.0001625512,0.0009734593,0.7025957,0.007860428,0.009447769,0.2750456],"study_design_scores_gemma":[0.000497747,0.001803553,0.01029197,0.0004976122,0.0006537611,0.02964445,0.0003764436,0.09267811,0.6363936,0.02173609,0.204945,0.0004816267],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0335886,0.007469564,0.9408886,0.004355601,0.001069263,0.0002609452,0.0002669726,0.00241726,0.009683219],"genre_scores_gemma":[0.07715914,0.005845953,0.9057395,0.001604719,0.000749988,0.000264876,0.0002243574,0.0006077591,0.00780373],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006971278,"threshold_uncertainty_score":0.02332121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02518756736109199,"score_gpt":0.2900196451491559,"score_spread":0.2648320777880639,"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."}}