{"id":"W2885067380","doi":"10.1007/s00234-018-2066-5","title":"Detection of early infarction signs with machine learning-based diagnosis by means of the Alberta Stroke Program Early CT score (ASPECTS) in the clinical routine","year":2018,"lang":"en","type":"article","venue":"Neuroradiology","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":96,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Medicine; Inter-rater reliability; Infarction; Thrombolysis; Neuroradiology; Neurology; Stroke (engine); Radiology; Neurosurgery; Ischemia; Internal medicine; Rating scale; Myocardial infarction","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001694557,0.0007926361,0.0007490384,0.002910191,0.0002942448,0.001365544,0.0006200094,0.0006879416,0.001012239],"category_scores_gemma":[0.005326536,0.0002354966,0.0003912046,0.001220073,0.0003692498,0.0008522142,0.0006172918,0.0007458307,0.0003370461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003639572,"about_ca_system_score_gemma":0.001057102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004603867,"about_ca_topic_score_gemma":0.008589745,"domain_scores_codex":[0.9990116,0.000275367,0.0001312893,0.0001129643,0.000366844,0.0001020254],"domain_scores_gemma":[0.9983228,0.0005120533,0.0003124488,0.00009574879,0.0005323443,0.0002247119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001165259,0.0002908716,0.9270063,0.0000981521,0.0001755607,0.0005649712,0.00007872818,0.001427731,0.003652463,0.0002829444,0.00194143,0.06331566],"study_design_scores_gemma":[0.0001281043,0.001047581,0.9408454,0.0001669735,0.0004299861,0.004477508,0.0003200123,0.04011441,0.007617489,0.00234685,0.002431693,0.00007399922],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9818572,0.002602898,0.008341309,0.0006068833,0.0001490847,0.0001503757,0.0006547543,0.0002534508,0.005384051],"genre_scores_gemma":[0.9872236,0.0009498006,0.01030726,0.0001324636,0.0001561291,0.00003946392,0.0005749487,0.00001530252,0.0006009033],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004603867,"threshold_uncertainty_score":0.009154141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02133164910771979,"score_gpt":0.2847367503337843,"score_spread":0.2634051012260645,"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."}}