{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004120429,0.0007143129,0.0004488657,0.000564499,0.0003555364,0.001067959,0.001279139,0.0007042593,0.01889336],"category_scores_gemma":[0.001031315,0.0008402086,0.0009054556,0.0002896473,0.0002966781,0.0005188099,0.001958967,0.0007763536,0.001869589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003114954,"about_ca_system_score_gemma":0.001345531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00156194,"about_ca_topic_score_gemma":0.003245956,"domain_scores_codex":[0.9995951,0.0000826794,0.00002235705,0.0000549928,0.0001963346,0.00004842012],"domain_scores_gemma":[0.9995703,0.0001680376,0.00002777326,0.0001258783,0.00005436538,0.00005371361],"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":[0.001535283,0.0007619938,0.01018806,0.0004707751,0.0001967366,0.003248915,0.001081696,0.261542,0.1089409,0.004524579,0.0162236,0.5912854],"study_design_scores_gemma":[0.000124747,0.0007600363,0.009088068,0.0001007865,0.0001837219,0.009610609,0.0004933972,0.8620809,0.07544252,0.005724635,0.03608467,0.0003057474],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1584642,0.0005041946,0.8113441,0.0002460938,0.0002730359,0.0003231336,0.001023466,0.007267616,0.02055411],"genre_scores_gemma":[0.6995202,0.0003701442,0.2898555,0.0001228132,0.0000501883,0.0001900127,0.0006079655,0.0009234254,0.008359768],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01889336,"threshold_uncertainty_score":0.06320459,"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."}}