{"id":"W2168199290","doi":"10.1016/j.compbiomed.2014.12.012","title":"Automated peroperative assessment of stents apposition from OCT pullbacks","year":2015,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Coronary Interventions and Diagnostics","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hôpital Notre-Dame","funders":"","keywords":"Optical coherence tomography; Segmentation; Computer science; Apposition; Algorithm; Stent; Artificial intelligence; Thresholding; Computer vision; Radiology; Medicine; Anatomy","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":[0.000882979,0.0008055841,0.0006190526,0.005077052,0.0003039267,0.001302324,0.0005474023,0.001292648,0.002785909],"category_scores_gemma":[0.003337673,0.0005403272,0.0003862428,0.0008690841,0.0002340079,0.000788178,0.0007593019,0.000556477,0.00126736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001771796,"about_ca_system_score_gemma":0.000279146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001124799,"about_ca_topic_score_gemma":0.002067804,"domain_scores_codex":[0.999374,0.0001238819,0.00008032614,0.00007847976,0.0002599449,0.00008345192],"domain_scores_gemma":[0.997557,0.0009308202,0.0003072911,0.0002265714,0.000804417,0.0001738964],"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":[0.009269382,0.0003952307,0.2985405,0.001089197,0.0003609786,0.0102679,0.0006939049,0.002414006,0.3647183,0.0005690443,0.003492893,0.3081887],"study_design_scores_gemma":[0.0001615352,0.001370984,0.7228025,0.0005328662,0.0008374788,0.04221399,0.00105178,0.06573202,0.1582365,0.0009963063,0.005764642,0.000299459],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9382216,0.005147961,0.04568632,0.0002713726,0.0002046249,0.0002618063,0.002037466,0.001517073,0.006651705],"genre_scores_gemma":[0.9628196,0.001997805,0.03173804,0.0002136446,0.00026551,0.0001106597,0.0008591096,0.0001838427,0.001811876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005077052,"threshold_uncertainty_score":0.009319842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0430141423339964,"score_gpt":0.401418536440203,"score_spread":0.3584043941062066,"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."}}