{"id":"W7162077383","doi":"10.82308/4957","title":"Making cough count: The application of cough as a biomarker for respiratory disease screening and monitoring","year":2025,"lang":"en","type":"dissertation","venue":"","topic":"Respiratory and Cough-Related Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Lung disease; Quality of Life Research; Respiratory disease; Medical screening; Multicenter study","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":[0.002403255,0.001379783,0.001329909,0.002977081,0.0005063781,0.003425343,0.001137858,0.001847711,0.002520844],"category_scores_gemma":[0.01007436,0.0005106469,0.001189034,0.001851733,0.0007914142,0.002330018,0.001517938,0.001220069,0.00147611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000578963,"about_ca_system_score_gemma":0.001126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002610897,"about_ca_topic_score_gemma":0.002948573,"domain_scores_codex":[0.9974376,0.0007681223,0.0002088117,0.0006543553,0.0007862133,0.0001449194],"domain_scores_gemma":[0.9958763,0.002288209,0.0006106226,0.000268145,0.0007629755,0.0001937172],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008855054,0.0003283084,0.1017191,0.002218928,0.0005903355,0.0005125594,0.0008121306,0.02543318,0.04757312,0.008092037,0.009295797,0.8025391],"study_design_scores_gemma":[0.0001514865,0.002665391,0.1847581,0.001789341,0.001413895,0.002995113,0.002245346,0.5816237,0.08205926,0.04788344,0.09163409,0.0007807945],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2289093,0.04974357,0.6782956,0.006619247,0.00309817,0.0007228703,0.004236969,0.003726559,0.02464776],"genre_scores_gemma":[0.7369785,0.0189877,0.2292304,0.001136668,0.001777007,0.0005576265,0.001814483,0.0002234979,0.009294129],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003425343,"threshold_uncertainty_score":0.0127098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09123538217682921,"score_gpt":0.4230698159889587,"score_spread":0.3318344338121295,"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."}}