{"id":"W2989616348","doi":"10.1016/j.joms.2019.11.013","title":"In-House Surgeon-Led Virtual Surgical Planning for Maxillofacial Reconstruction","year":2019,"lang":"en","type":"article","venue":"Journal of Oral and Maxillofacial Surgery","topic":"Anatomy and Medical Technology","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; McGill University Health Centre","funders":"","keywords":"Medicine; Stereolithography; DICOM; 3d printer; 3D printing; 3d printed; Three dimensional printing; Surgical planning; Biomedical engineering; Computer graphics (images); Surgery; Computer science; Radiology; Engineering","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":[0.0005908918,0.00016267,0.0005742877,0.000359388,0.00004215556,0.00001980179,0.00007909225,0.0002618819,0.000106297],"category_scores_gemma":[0.0001174561,0.0001400653,0.0001965697,0.0001866173,0.00008550195,0.000338906,0.00002167661,0.0003406509,0.000009424869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004857587,"about_ca_system_score_gemma":0.00005285141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001420137,"about_ca_topic_score_gemma":0.000001721171,"domain_scores_codex":[0.9987565,0.00001613573,0.0005944069,0.0001293312,0.000173325,0.0003302858],"domain_scores_gemma":[0.9995381,0.00007216493,0.0001261146,0.00006726049,0.00005688454,0.0001395069],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005708709,0.00004939102,0.0822954,0.00007543872,0.00009544894,0.0004820436,0.0001154345,0.0002982843,0.0005283248,0.001871382,0.0004238781,0.9131941],"study_design_scores_gemma":[0.001535365,0.0001813883,0.0051698,0.00009937774,0.00002451471,0.001275091,0.0002034127,0.0009249209,0.0007681908,0.001533814,0.9879747,0.0003094418],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950815,0.0007606215,0.0003400686,0.0001767829,0.003174885,0.0001159928,0.000005708309,0.00008123726,0.0002631841],"genre_scores_gemma":[0.9978817,0.001586574,0.0002478394,0.00005936455,0.00009768228,0.000005300187,0.000001790158,0.00003291663,0.00008690195],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9875508,"threshold_uncertainty_score":0.5711697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01120597351760816,"score_gpt":0.2348877219582724,"score_spread":0.2236817484406643,"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."}}