{"id":"W3211084592","doi":"10.1088/1361-6560/ac36a2","title":"Fibro-CoSANet: Pulmonary Fibrosis Prognosis Prediction using a Convolutional Self Attention Network","year":2021,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto; Toronto Metropolitan University; University Health Network","funders":"","keywords":"Pulmonary function testing; Lung; Idiopathic pulmonary fibrosis; Medicine; Interstitial lung disease; Pulmonary fibrosis; Fibrosis; Lung function; Convolutional neural network; Internal medicine; Cardiology; Computer science; Artificial intelligence","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":[0.0005367768,0.001067467,0.0004911539,0.0007051757,0.0003399777,0.0004456149,0.0009891108,0.000963299,0.002146742],"category_scores_gemma":[0.001068257,0.0002638298,0.0006839927,0.0003832447,0.0002272719,0.0006042703,0.0007749075,0.0008143337,0.0007347777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007662062,"about_ca_system_score_gemma":0.0007714675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01164549,"about_ca_topic_score_gemma":0.01844418,"domain_scores_codex":[0.9998222,0.00002772547,0.000007728225,0.00007154174,0.00003263932,0.00003812388],"domain_scores_gemma":[0.9997827,0.0000772083,0.00002279002,0.00002836226,0.00006340832,0.00002550728],"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":[0.000847421,0.0007252629,0.02220081,0.0001699118,0.0003983204,0.0006308077,0.0001066715,0.3251208,0.0169585,0.003187577,0.0476232,0.5820307],"study_design_scores_gemma":[0.00001886366,0.00006715589,0.001173771,0.000009724861,0.0000269798,0.0000698448,0.000006639069,0.9941502,0.002059121,0.001365194,0.001044388,0.00000832051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3953452,0.004079472,0.5546706,0.00270313,0.0007697905,0.000367463,0.008570075,0.02017013,0.0133242],"genre_scores_gemma":[0.8785055,0.0005901142,0.09587506,0.0009447641,0.0002691741,0.000176413,0.01121,0.0002122056,0.01221679],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01164549,"threshold_uncertainty_score":0.02315545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03798682638965695,"score_gpt":0.1870099971994617,"score_spread":0.1490231708098048,"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."}}