{"id":"W4402830242","doi":"10.1109/access.2024.3467266","title":"Automated Detection of Acute Respiratory Distress Using Temporal Visual Information","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine; Université du Québec à Montréal","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Acute respiratory distress; Respiratory distress; Computer vision; Medicine; Internal medicine; Lung; Radiology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001729651,0.0001184194,0.0001933093,0.0003668382,0.00005467503,0.0001416443,0.0001039957,0.0001036298,0.00002031616],"category_scores_gemma":[0.0000573764,0.0001092412,0.00007549762,0.0006133693,0.00005162272,0.00117922,0.00004534805,0.000142691,0.00002275314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002195712,"about_ca_system_score_gemma":0.0002002959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003500789,"about_ca_topic_score_gemma":0.0000182394,"domain_scores_codex":[0.9990122,0.00003264223,0.0003651367,0.0001521818,0.0002888329,0.0001490353],"domain_scores_gemma":[0.9994282,0.00007332287,0.0001122685,0.0001846076,0.0001364888,0.00006508331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007471538,0.0004329171,0.03155722,0.005642824,0.000982093,0.0003317915,0.001527619,0.002689518,0.8391395,0.00002880809,0.02209353,0.094827],"study_design_scores_gemma":[0.001021889,0.000279833,0.02735168,0.001248057,0.0005694867,0.00004465675,0.00004173251,0.32959,0.6165302,0.00002254069,0.02303412,0.0002657253],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9924142,0.00008968399,0.005057714,0.0004439449,0.0009706322,0.000260747,0.00004035194,0.0006896968,0.00003298931],"genre_scores_gemma":[0.9986807,0.00000682608,0.00006832051,0.001006475,0.0001644097,0.00001414743,0.00002962477,0.00001906701,0.00001041732],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3269005,"threshold_uncertainty_score":0.4454726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04519333431633694,"score_gpt":0.4090088491942213,"score_spread":0.3638155148778844,"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."}}