{"id":"W4416406986","doi":"10.1111/iej.70065","title":"Artificial Intelligence in the Detection of Clinically Negotiable Second Mesio‐Buccal Canals in Periapical Images of Maxillary Molars","year":2025,"lang":"en","type":"article","venue":"International Endodontic Journal","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Molar; Maxillary molar; Endodontics; Endodontic therapy","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00124842,0.00010512,0.0002389402,0.0005747938,0.00004064927,0.00008172639,0.0005452367,0.00006868827,0.0004072207],"category_scores_gemma":[0.0005304332,0.00008662373,0.0002090945,0.0004408645,0.0001415835,0.0002693102,0.00006209724,0.0005536077,0.000004889202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008214255,"about_ca_system_score_gemma":0.0001012952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003451306,"about_ca_topic_score_gemma":0.0008288912,"domain_scores_codex":[0.9977817,0.000209298,0.001238075,0.0001594369,0.0004312642,0.0001802429],"domain_scores_gemma":[0.9989681,0.0004255307,0.000305702,0.0001238879,0.0001432325,0.00003351368],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001729675,0.001198598,0.7486835,0.0001209121,0.0004612551,0.001365496,0.001034879,0.001198681,0.1269183,0.008118771,0.0007195934,0.1084504],"study_design_scores_gemma":[0.0009520773,0.0001735245,0.8749799,0.0005720037,0.00007183642,0.001999449,0.003389693,0.006394507,0.08381506,0.0264398,0.000969465,0.0002427448],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9772997,0.0004905687,0.01601476,0.0003380767,0.002173495,0.0001078175,0.00001740221,0.000004783234,0.00355338],"genre_scores_gemma":[0.9990198,0.00008436379,0.0005106829,0.00013783,0.0001220383,0.000004224667,0.000002126661,0.000006074451,0.0001128655],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1262963,"threshold_uncertainty_score":0.4458781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01712630261502716,"score_gpt":0.3229368825549867,"score_spread":0.3058105799399595,"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."}}