{"id":"W4407425487","doi":"10.1016/j.jdent.2025.105622","title":"Automated pediatric TMJ articular disk identification and displacement classification in MRI with machine learning","year":2025,"lang":"en","type":"article","venue":"Journal of Dentistry","topic":"Temporomandibular Joint Disorders","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Faculty of Medicine and Dentistry, University of Alberta","keywords":"Displacement (psychology); Identification (biology); Orthodontics; Computer science; Artificial intelligence; Biomedical engineering; Medicine; Materials science; Psychology; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009133953,0.0001001301,0.0002071597,0.0002989251,0.0002464494,0.00002900082,0.00009575176,0.00009693209,0.00003292294],"category_scores_gemma":[0.0001980123,0.00008162572,0.00003247803,0.0003752617,0.00002987483,0.000198124,0.00004068909,0.0006075052,0.00001270813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001597368,"about_ca_system_score_gemma":0.0001692445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002676037,"about_ca_topic_score_gemma":0.0001414187,"domain_scores_codex":[0.9982758,0.0003548207,0.0007847173,0.0001450579,0.0002527618,0.0001868273],"domain_scores_gemma":[0.9988137,0.0001242762,0.0007361932,0.0001335745,0.0001299773,0.00006226071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001011043,0.0001017271,0.9950483,0.000284608,0.00003838202,0.00004462577,0.0004257788,0.0001746935,0.001690737,0.0002268216,0.00163582,0.0002273933],"study_design_scores_gemma":[0.001791636,0.00005448791,0.9871935,0.0002506121,0.0001100217,0.00002518962,0.001715892,0.007153511,0.000047234,0.0001296458,0.001447397,0.0000809126],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9866308,0.001045106,0.009398915,0.002048768,0.0003684471,0.0002860765,0.000002852213,0.00004498151,0.0001740504],"genre_scores_gemma":[0.9980331,0.0003194645,0.0005189038,0.00003726708,0.00004376303,0.00001562098,0.00001681597,0.00001122901,0.001003811],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01140232,"threshold_uncertainty_score":0.33286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01777761517097171,"score_gpt":0.3714020819776368,"score_spread":0.3536244668066651,"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."}}