{"id":"W4213173536","doi":"10.1016/j.ajodo.2021.03.017","title":"Machine learning in orthodontics: Automated facial analysis of vertical dimension for increased precision and efficiency","year":2022,"lang":"en","type":"article","venue":"American Journal of Orthodontics and Dentofacial Orthopedics","topic":"Orthodontics and Dentofacial Orthopedics","field":"Dentistry","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Montreal Children's Hospital","funders":"","keywords":"Computer science; Reliability (semiconductor); Orthodontics; Intraclass correlation; Artificial intelligence; Inter-rater reliability; Standard deviation; Confidence interval; Reproducibility; Mathematics; Statistics; Medicine; Power (physics); Rating scale","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":[0.001391841,0.0005285254,0.0007585938,0.001225502,0.0003658668,0.0009810789,0.0008941052,0.000711311,0.003751994],"category_scores_gemma":[0.00383106,0.0004320049,0.0005843103,0.001396478,0.0002983294,0.0007223419,0.0007037836,0.0007623709,0.001822235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003429367,"about_ca_system_score_gemma":0.0007630745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003825581,"about_ca_topic_score_gemma":0.007135674,"domain_scores_codex":[0.9989777,0.0003131485,0.00006866101,0.000189771,0.0003917957,0.00005894771],"domain_scores_gemma":[0.9981202,0.0009280586,0.0001692276,0.0002914964,0.0004631325,0.00002791785],"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":[0.0001917901,0.00008432212,0.004434587,0.00008510082,0.00006559703,0.00003248667,0.00004224286,0.0228638,0.02648396,0.001837006,0.003632711,0.9402465],"study_design_scores_gemma":[0.00004044371,0.00008781139,0.01222007,0.00003406234,0.00006228482,0.0002659203,0.00004852634,0.9472482,0.02877888,0.005271226,0.005901821,0.00004082635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04812187,0.001073069,0.9434492,0.0004670738,0.0001361815,0.00008253878,0.0005043212,0.003401597,0.002764113],"genre_scores_gemma":[0.3318283,0.0006489899,0.6627499,0.0001417402,0.0001395496,0.000106072,0.0006159815,0.0003099536,0.003459456],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003825581,"threshold_uncertainty_score":0.01255167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01393794571085507,"score_gpt":0.2929168442223053,"score_spread":0.2789788985114502,"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."}}