{"id":"W3208111848","doi":"10.1097/prs.0000000000008422","title":"SMaRT Assessment Tool: An Innovative Approach for Objective Assessment of Flap Designs","year":2021,"lang":"en","type":"article","venue":"Plastic & Reconstructive Surgery","topic":"Digital Imaging in Medicine","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University Health Centre","funders":"","keywords":"Computer science; Ideal (ethics); Artificial intelligence; Simulation; Engineering drawing; Engineering","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":[0.008599414,0.00186994,0.0009342034,0.006589591,0.0003064616,0.001735737,0.001296256,0.0009003463,0.01323281],"category_scores_gemma":[0.03198441,0.0005690454,0.00115714,0.001386428,0.0005928402,0.00138392,0.002630362,0.001011628,0.002620724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005016276,"about_ca_system_score_gemma":0.001109379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006024121,"about_ca_topic_score_gemma":0.001220739,"domain_scores_codex":[0.9934443,0.002303183,0.0007345166,0.0004840127,0.002880673,0.0001534052],"domain_scores_gemma":[0.9758445,0.01447773,0.001945055,0.001243788,0.005953924,0.0005351236],"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":[0.0009755269,0.0007085095,0.01682787,0.001034192,0.0001730412,0.000272569,0.001119805,0.006949283,0.04135424,0.003415731,0.0178008,0.9093684],"study_design_scores_gemma":[0.001221596,0.006857663,0.1306471,0.001928059,0.0007716527,0.00481214,0.002780967,0.5198605,0.1804936,0.02531213,0.1240562,0.001258435],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0684379,0.0003185383,0.9003477,0.0004226387,0.0003011916,0.003661681,0.002643369,0.01397298,0.009894025],"genre_scores_gemma":[0.1310738,0.00020972,0.8599658,0.0002059819,0.00007057658,0.003860676,0.0009451326,0.0007136117,0.002954813],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01323281,"threshold_uncertainty_score":0.04547858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05439996608605038,"score_gpt":0.3345848755497554,"score_spread":0.280184909463705,"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."}}