{"id":"W3128013324","doi":"10.1093/ehjci/jeaa356.259","title":"Application of a machine learning contouring tool for the evaluation of left ventricular strain in clinical practice","year":2021,"lang":"en","type":"article","venue":"European Heart Journal - Cardiovascular Imaging","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Circle Cardiovascular Imaging","funders":"European Regional Development Fund; Engineering and Physical Sciences Research Council; Barts Health NHS Trust; Barts Charity; UK Research and Innovation","keywords":"General partnership; Christian ministry; Government (linguistics); Champion; Medicine; Health care; Management; Political science; Medical education; Business; Finance; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02270944,0.0001171046,0.0005065293,0.0001040926,0.0001079978,0.00004464114,0.0000776513,0.00002216243,0.000008697027],"category_scores_gemma":[0.01751499,0.00009791962,0.00122997,0.0001985175,0.00006172476,0.0001357138,0.00005606283,0.0005822329,0.000003419796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007842638,"about_ca_system_score_gemma":0.0002422575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001966913,"about_ca_topic_score_gemma":6.171274e-7,"domain_scores_codex":[0.9957221,0.002104965,0.000824916,0.0002294044,0.0009196172,0.0001990398],"domain_scores_gemma":[0.9964006,0.001363145,0.0002793373,0.0004279691,0.001459213,0.00006976342],"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":[0.0001625426,0.0002989635,0.3507394,0.0002037035,0.002120002,0.0008046628,0.0006391134,0.06319734,0.002796996,0.00003313659,0.000341983,0.5786622],"study_design_scores_gemma":[0.007784789,0.00008914348,0.783964,0.0006088377,0.005649169,0.01070014,0.001402248,0.07132429,0.001405616,0.00003989094,0.1168127,0.0002192488],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2765178,0.2596587,0.4518618,0.004993693,0.001256229,0.002289601,0.0000359848,0.00006080794,0.003325348],"genre_scores_gemma":[0.9927563,0.0005556131,0.005769245,0.0002127152,0.0006384614,0.0000052002,0.00001650953,0.0000364156,0.000009541553],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7162385,"threshold_uncertainty_score":0.9907609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04691967774171765,"score_gpt":0.3688972492362679,"score_spread":0.3219775714945503,"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."}}