{"id":"W4224731106","doi":"10.1155/2022/2014349","title":"Object-Specific Four-Path Network for Stroke Risk Stratification of Carotid Arteries in Ultrasound Images","year":2022,"lang":"en","type":"article","venue":"Computational and Mathematical Methods in Medicine","topic":"Cerebrovascular and Carotid Artery Diseases","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Artificial intelligence; Upsampling; Feature extraction; Pooling; Stroke (engine); Computer science; Feature (linguistics); Radiology; Pattern recognition (psychology); Context (archaeology); Medicine; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001732778,0.000128762,0.0005104588,0.000141868,0.00008658286,0.000006791945,0.00006503928,0.00002953929,0.000248697],"category_scores_gemma":[0.0006150753,0.0001056316,0.00008395731,0.0002335891,0.000210729,0.00003851221,0.00003255337,0.0001827408,4.499072e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003930293,"about_ca_system_score_gemma":0.00005315039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007813361,"about_ca_topic_score_gemma":0.000002009218,"domain_scores_codex":[0.9983535,0.0003526891,0.0005596674,0.0002451516,0.0003202455,0.0001687063],"domain_scores_gemma":[0.9970254,0.002531731,0.0001354773,0.0001490085,0.00007862013,0.00007976416],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002311405,0.004871585,0.4062526,0.007665268,0.0008587306,0.00004491888,0.01930642,0.05261789,0.04366738,0.2861467,0.01141016,0.1648469],"study_design_scores_gemma":[0.00519994,0.001146092,0.4383026,0.0004336776,0.0002273004,0.0002189346,0.005169244,0.01014027,0.0005232829,0.5378018,0.0005829717,0.0002538959],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1711459,0.00113116,0.82457,0.0006028529,0.0001631066,0.00132477,0.0001062481,0.00002657711,0.0009294224],"genre_scores_gemma":[0.5103835,0.0001163622,0.4888183,0.0001281737,0.0001342395,0.0002411454,0.0001065308,0.00001476108,0.00005689299],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3392377,"threshold_uncertainty_score":0.4307533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03844185506170542,"score_gpt":0.3517646242134989,"score_spread":0.3133227691517935,"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."}}