{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002657806,0.0006759024,0.0003906184,0.00103481,0.0001228997,0.0004921387,0.000410255,0.0004655793,0.0007041888],"category_scores_gemma":[0.001194856,0.0001730687,0.0003865941,0.0003769617,0.0001198488,0.0004866467,0.0004642146,0.0004241839,0.0004082824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003565251,"about_ca_system_score_gemma":0.0004319937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004956516,"about_ca_topic_score_gemma":0.006368834,"domain_scores_codex":[0.9997457,0.00003264959,0.00001396056,0.00008361602,0.00007339827,0.00005071467],"domain_scores_gemma":[0.999676,0.00009601024,0.00005142319,0.00002574441,0.0001183875,0.00003244073],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008904714,0.0004514061,0.02623987,0.0002402853,0.0001384601,0.0004603219,0.000090127,0.05589273,0.09713572,0.0005463204,0.006706412,0.8112078],"study_design_scores_gemma":[0.00002086515,0.0002245769,0.02085185,0.00002605318,0.00004796705,0.0003239285,0.00004825095,0.9467571,0.02970626,0.0005160864,0.001457168,0.00001982967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5191244,0.002599748,0.4673823,0.0005278966,0.0002668497,0.0002355444,0.002065863,0.004189493,0.003607839],"genre_scores_gemma":[0.9171255,0.0007717996,0.07791262,0.000185356,0.00009613187,0.00006346855,0.002095938,0.00004710846,0.001702055],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004956516,"threshold_uncertainty_score":0.00985533,"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."}}