{"id":"W2888273372","doi":"10.1177/1055665618796012","title":"Clinical Application of a New Approach to Identify Oral–Nasal Balance Disorders Based on Nasalance Scores","year":2018,"lang":"en","type":"article","venue":"The Cleft Palate-Craniofacial Journal","topic":"Obstructive Sleep Apnea Research","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Nasality; Linear discriminant analysis; Centroid; Discriminant function analysis; Balance (ability); Medicine; Audiology; Mathematics; Artificial intelligence; Statistics; Speech recognition; Physical therapy; Computer science","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":[0.002825031,0.000893323,0.0004986816,0.003768345,0.000304826,0.0008654589,0.0005559813,0.0005273824,0.001255323],"category_scores_gemma":[0.008254413,0.0001880542,0.0005224085,0.000688058,0.000513379,0.0008819569,0.0009299735,0.0006417028,0.0007099403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006095617,"about_ca_system_score_gemma":0.0006535081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001907676,"about_ca_topic_score_gemma":0.002885601,"domain_scores_codex":[0.9983686,0.0003931808,0.0002525338,0.0003331765,0.0005865393,0.0000660783],"domain_scores_gemma":[0.9967494,0.001111237,0.0005118673,0.000209703,0.001261977,0.000155782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008333023,0.0002149349,0.671577,0.0002515431,0.0003038295,0.0003236413,0.0003238625,0.00348001,0.01938929,0.000318473,0.001278132,0.3017059],"study_design_scores_gemma":[0.0002323302,0.001947786,0.8759605,0.0001661528,0.000292703,0.005432481,0.0005548319,0.09280396,0.01640032,0.001290766,0.004780449,0.0001377811],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8103209,0.002179951,0.1790282,0.0005187903,0.0002360984,0.0006876198,0.001248307,0.0006362625,0.005143825],"genre_scores_gemma":[0.9237017,0.0003414098,0.074063,0.00007231902,0.00006419618,0.0002546924,0.0005304513,0.0000248096,0.0009474452],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003768345,"threshold_uncertainty_score":0.01494038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04157846146325751,"score_gpt":0.3884248485463403,"score_spread":0.3468463870830828,"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."}}