{"id":"W2946038147","doi":"10.1002/lary.28082","title":"Intraoperative cone‐beam CT‐guided osteotomy navigation in mandible and maxilla surgery","year":2019,"lang":"en","type":"article","venue":"The Laryngoscope","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto","funders":"Princess Margaret Cancer Foundation","keywords":"Medicine; Cadaveric spasm; Cone beam computed tomography; Cone beam ct; Cadaver; Osteotomy; Maxilla; Mandible (arthropod mouthpart); Navigation system; Image-guided surgery; Radiology; Surgery; Nuclear medicine; Orthodontics; Computed tomography; Artificial intelligence; Computer science","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":[0.0004709748,0.0001514205,0.0002504441,0.0001296179,0.0000941593,0.0001395376,0.0001404881,0.00003502953,0.0003566395],"category_scores_gemma":[0.00002643228,0.0001145527,0.00006808558,0.0004000541,0.0001040454,0.0004022932,0.000081369,0.0002140538,0.0005402799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002937766,"about_ca_system_score_gemma":0.00002247241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004361043,"about_ca_topic_score_gemma":0.00004272331,"domain_scores_codex":[0.9989083,0.0001211228,0.0002689807,0.0002587827,0.0001823897,0.0002604375],"domain_scores_gemma":[0.9993982,0.0001833994,0.00007666815,0.0002613901,0.00003073242,0.00004960291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006371061,0.000060093,0.9737206,0.000106748,0.0000643333,0.0002615615,0.0007846451,0.00001869398,0.01731045,0.0008017955,0.004363373,0.002444017],"study_design_scores_gemma":[0.002198897,0.00006494984,0.9125454,0.0005959274,0.00005189081,0.001263375,0.001628166,0.0006772372,0.06931857,0.001211395,0.00982774,0.0006164955],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9890007,0.0008932251,0.000009011621,0.000212138,0.000528449,0.0004264023,0.00001182524,0.00004633674,0.008871873],"genre_scores_gemma":[0.9979562,0.00009919663,0.00003084495,0.0003999488,0.00004739544,0.00002463407,0.0000360993,0.00002028436,0.001385387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06117523,"threshold_uncertainty_score":0.6944385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01346902209896721,"score_gpt":0.2601266804453997,"score_spread":0.2466576583464324,"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."}}