{"id":"W3010251753","doi":"10.1177/0846537120913497","title":"COVID-19: What Can We Learn From Stories From the Trenches?","year":2020,"lang":"en","type":"article","venue":"Canadian Association of Radiologists Journal","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Medicine; Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Pandemic; Coronavirus Infections; Betacoronavirus; MEDLINE; Virology; Internal medicine; Infectious disease (medical specialty)","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":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006890626,0.0001828971,0.0004803333,0.0001087653,0.0003440205,0.0001899693,0.0003270872,0.0002549288,0.0009940937],"category_scores_gemma":[0.01876686,0.0001448675,0.0001989576,0.0003002577,0.0001387382,0.0001811341,0.00002274341,0.0007474943,0.00002414383],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.006008527,"about_ca_system_score_gemma":0.006788043,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1224612,"about_ca_topic_score_gemma":0.2163704,"domain_scores_codex":[0.9980026,0.0004226852,0.0004629462,0.0002729033,0.000442734,0.000396076],"domain_scores_gemma":[0.9948833,0.002104754,0.0006780465,0.000239308,0.0002329092,0.001861663],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006498126,0.00002210068,0.3284957,0.00001555172,0.0004748536,0.0002026685,0.01574157,0.0003731876,0.0002337178,0.00006896758,0.6485904,0.005716283],"study_design_scores_gemma":[0.001775136,0.0001839873,0.1426937,0.0001260292,0.0002638869,0.00002692816,0.004238338,0.0002017139,0.0001006653,0.0008221312,0.8493653,0.0002022386],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1437776,0.003560646,0.0001684441,0.8508983,0.0008732735,0.0001834185,0.0004392191,0.00004001826,0.00005906865],"genre_scores_gemma":[0.7932367,0.003005414,0.0002953332,0.2010796,0.001942754,0.000005976968,0.0001476716,0.00002464781,0.0002619282],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.6498187,"threshold_uncertainty_score":0.9999191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0515421160789499,"score_gpt":0.3026088378815626,"score_spread":0.2510667218026127,"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."}}