{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007672659,0.00019455,0.0002933318,0.0001551517,0.0009820323,0.0001388829,0.0001799724,0.0000404198,0.0004391293],"category_scores_gemma":[0.0001053846,0.0001750007,0.002362511,0.0002059356,0.0001219546,0.0001241156,0.0001355357,0.000188497,0.000005626671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001194833,"about_ca_system_score_gemma":0.00004671725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002471735,"about_ca_topic_score_gemma":0.00001135058,"domain_scores_codex":[0.998226,0.0002776667,0.0004858443,0.0004138681,0.0003381858,0.0002583809],"domain_scores_gemma":[0.9988007,0.00009668183,0.0001066852,0.0006432618,0.0002133137,0.0001393446],"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":[0.0003173059,0.001576652,0.002358105,0.0004002989,0.001301501,0.0001632819,0.0002511733,0.001268707,0.0002004048,0.001123934,0.001364418,0.9896742],"study_design_scores_gemma":[0.01449769,0.009184903,0.05221431,0.001412514,0.007588911,0.01122226,0.008866322,0.2766759,0.0002165924,0.003908238,0.6125885,0.001623824],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07081921,0.02668419,0.8950154,0.001437368,0.001916753,0.002699335,0.0007376579,0.0003111557,0.0003789799],"genre_scores_gemma":[0.9922259,0.0002357726,0.003038422,0.0002773853,0.0001329478,0.0009167268,0.00169705,0.00006027592,0.001415476],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9880504,"threshold_uncertainty_score":0.7553098,"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."}}