{"id":"W4313177521","doi":"10.2139/ssrn.4230195","title":"Predicting Total Lung Capacity from Spirometry: A Machine Learning Approach","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Chronic Obstructive Pulmonary Disease (COPD) Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Spirometry; Medicine; Computer science; Internal medicine","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":[0.001842983,0.00118252,0.001970832,0.002287071,0.0004595641,0.001452747,0.001460335,0.001691308,0.001613847],"category_scores_gemma":[0.004719411,0.0003912094,0.001420332,0.00132188,0.0003273352,0.0008687161,0.0006300053,0.001929101,0.001033992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004653827,"about_ca_system_score_gemma":0.0007745672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005062402,"about_ca_topic_score_gemma":0.003708378,"domain_scores_codex":[0.9993116,0.0002359205,0.00007349105,0.0002040906,0.0001069831,0.00006800506],"domain_scores_gemma":[0.9971542,0.002267082,0.0001359414,0.0001061089,0.0002632884,0.00007323797],"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":[0.0008964476,0.002450907,0.1192701,0.0002351772,0.0007814147,0.0003291014,0.00009259255,0.2548422,0.005452925,0.001485775,0.004467858,0.6096955],"study_design_scores_gemma":[0.00002641164,0.000167778,0.01203739,0.00002305791,0.00007252579,0.0001124717,0.00002629517,0.9841681,0.0006919756,0.002347225,0.0003035729,0.0000232353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4434208,0.006039397,0.5381617,0.001980553,0.0004083989,0.0002938135,0.002297267,0.001800747,0.005597297],"genre_scores_gemma":[0.9464092,0.0008897395,0.04887569,0.0002176625,0.0003290659,0.0001364668,0.001261767,0.00003035742,0.00185009],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005062402,"threshold_uncertainty_score":0.01006585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01266470938656637,"score_gpt":0.2549398563287312,"score_spread":0.2422751469421648,"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."}}