{"id":"W2564011550","doi":"10.3390/ijms17122110","title":"Validating Intravascular Imaging with Serial Optical Coherence Tomography and Confocal Fluorescence Microscopy","year":2016,"lang":"en","type":"article","venue":"International Journal of Molecular Sciences","topic":"Coronary Interventions and Diagnostics","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; National Research Council Canada; Polytechnique Montréal; Montreal Heart Institute","funders":"Canadian Institutes of Health Research","keywords":"Optical coherence tomography; Ex vivo; Intravascular ultrasound; In vivo; Histology; Biomedical engineering; Confocal; Microscopy; Fluorescence-lifetime imaging microscopy; Preclinical imaging; Pathology; Confocal microscopy; Fluorescence microscope; Molecular imaging; Tomography; Materials science; Medicine; Radiology; Fluorescence; Biology; Optics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003007485,0.0007053206,0.0003920614,0.001372391,0.0005127914,0.0007577412,0.0008889045,0.0009121608,0.001534151],"category_scores_gemma":[0.002999573,0.0007282009,0.0003894338,0.0005011487,0.0007886378,0.0008566199,0.0009440459,0.0005705772,0.0006153443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007540887,"about_ca_system_score_gemma":0.001248555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002908171,"about_ca_topic_score_gemma":0.003219514,"domain_scores_codex":[0.9982395,0.0004895443,0.00013446,0.0003614125,0.0006275391,0.0001475222],"domain_scores_gemma":[0.9972274,0.0006436265,0.0003577272,0.0006854688,0.0009843791,0.0001013187],"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":[0.000166245,0.00005648465,0.003336447,0.00009536627,0.00002025408,0.0001362972,0.000070511,0.001142177,0.9838191,0.0007915561,0.0001338929,0.01023162],"study_design_scores_gemma":[0.00002998859,0.0004972017,0.0144385,0.00003184117,0.00006581927,0.001256007,0.00007867368,0.02878401,0.9497845,0.0003832538,0.004605004,0.0000452279],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4618955,0.001540734,0.5298668,0.0002187672,0.00008876051,0.0005775496,0.0006179661,0.001580514,0.00361349],"genre_scores_gemma":[0.4564862,0.0008245058,0.5398676,0.0001380418,0.00004277094,0.0005873477,0.0005138395,0.0002604359,0.001279378],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003007485,"threshold_uncertainty_score":0.01590532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01004059567836064,"score_gpt":0.2950206505152062,"score_spread":0.2849800548368455,"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."}}