{"id":"W4317567738","doi":"10.1002/ccd.30395","title":"The MLD MAX OCT algorithm: An imaging‐based workflow for percutaneous coronary intervention","year":2022,"lang":"en","type":"article","venue":"Catheterization and Cardiovascular Interventions","topic":"Coronary Interventions and Diagnostics","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Medicine; Conventional PCI; Percutaneous coronary intervention; Optical coherence tomography; Workflow; Stent; Intravascular ultrasound; Radiology; Percutaneous; Algorithm; Medical physics; Internal medicine; Computer science; Myocardial infarction","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":[0.003004024,0.001386887,0.0007139021,0.002643014,0.0009057602,0.003386331,0.001740646,0.001245293,0.007451673],"category_scores_gemma":[0.006614437,0.0008848736,0.0009156718,0.001074125,0.0004865519,0.001350776,0.002095588,0.001860759,0.007473563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008659773,"about_ca_system_score_gemma":0.00363871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002812839,"about_ca_topic_score_gemma":0.003816283,"domain_scores_codex":[0.9988686,0.0002900441,0.000202287,0.0002153338,0.0003509993,0.00007273675],"domain_scores_gemma":[0.998577,0.000342712,0.000158401,0.0002065334,0.0005004547,0.0002149109],"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":[0.0008118118,0.0002326508,0.007270989,0.0003151823,0.0000915128,0.0006522668,0.0003790413,0.01678077,0.02188342,0.006904205,0.05661279,0.8880653],"study_design_scores_gemma":[0.000456717,0.0003642389,0.009181594,0.0003799884,0.0001437079,0.003290568,0.0003407508,0.7279665,0.05221636,0.02943536,0.1757126,0.000511715],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003550651,0.00026908,0.9729899,0.0006460591,0.000129529,0.0004220822,0.0005436642,0.01904893,0.002400067],"genre_scores_gemma":[0.01525787,0.0001996208,0.9808944,0.0002068445,0.00005645676,0.0004259681,0.0006959811,0.001274614,0.0009882717],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007451673,"threshold_uncertainty_score":0.02492839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02099045336352182,"score_gpt":0.2842379128467227,"score_spread":0.2632474594832008,"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."}}