{"id":"W4200418428","doi":"10.21203/rs.3.rs-1161801/v1","title":"Acoustic surveillance for respiratory diseases: a prospective analysis of cough trends using artificial intelligence.","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Respiratory and Cough-Related Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Generalitat de Catalunya; Ministerio de Ciencia e Innovación; Centres de Recerca de Catalunya","keywords":"Medicine; Incidence (geometry); Cohort; Population; Respiratory system; Coronavirus disease 2019 (COVID-19); Prospective cohort study; Intensive care medicine; Emergency medicine; Disease; Internal medicine; Environmental health; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001481269,0.000232068,0.0002380988,0.001317277,0.0001774945,0.0005755745,0.0003246459,0.0004323785,0.001421587],"category_scores_gemma":[0.004212595,0.0002339502,0.0003837437,0.001564606,0.0001484203,0.0004878441,0.0005816699,0.0003738803,0.0004595926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002476157,"about_ca_system_score_gemma":0.0002652791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007121752,"about_ca_topic_score_gemma":0.004252238,"domain_scores_codex":[0.9994266,0.0002681735,0.00006301344,0.0001059023,0.00008013801,0.0000560967],"domain_scores_gemma":[0.9978192,0.0007865388,0.0006589714,0.0002597185,0.0002994326,0.0001761417],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007470576,0.00003884494,0.9977298,0.00001128315,0.00003983992,0.00001684854,0.00003806845,0.00008208063,0.00008151934,0.00001100846,0.0001214909,0.001754618],"study_design_scores_gemma":[0.000005016261,0.00009925291,0.9985905,0.000004543039,0.00002767675,0.0000553663,0.0001328427,0.0008516737,0.00004528413,0.00002163443,0.0001637815,0.000002411725],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952082,0.0002193762,0.0005409942,0.00008743193,0.000007991198,0.00002465214,0.003434386,0.0000157898,0.0004613017],"genre_scores_gemma":[0.9965767,0.0001052166,0.0004700653,0.00002198566,0.00001460256,0.0000294574,0.002462955,0.00000433183,0.0003147428],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007121752,"threshold_uncertainty_score":0.01416057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1804015305956926,"score_gpt":0.478127540331583,"score_spread":0.2977260097358905,"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."}}