{"id":"W2990493624","doi":"10.1016/j.prosdent.2019.06.027","title":"Full digital workflow for crown lengthening by using a single surgical guide","year":2019,"lang":"en","type":"article","venue":"Journal of Prosthetic Dentistry","topic":"Dental Implant Techniques and Outcomes","field":"Dentistry","cited_by":54,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Crown lengthening; DICOM; Crown (dentistry); Workflow; Computer science; Margin (machine learning); Cone beam computed tomography; Dental alveolus; Software; Gingival margin; Surgical planning; Orthodontics; Computed tomography; Medicine; Artificial intelligence; Surgery","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":[0.0003868397,0.0002401811,0.0004965434,0.0001146586,0.00009665477,0.0005487403,0.000428359,0.0001813589,0.0002969795],"category_scores_gemma":[0.00008183163,0.0002062149,0.0004870833,0.0001263511,0.00008476417,0.0005785982,0.000118235,0.0002984137,0.0001266466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001591676,"about_ca_system_score_gemma":0.00006005952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004420649,"about_ca_topic_score_gemma":0.00000151766,"domain_scores_codex":[0.9978448,0.00003263741,0.0009289685,0.0002408757,0.0005306294,0.0004221609],"domain_scores_gemma":[0.9985572,0.0001406688,0.0006856472,0.0002608775,0.0001907944,0.0001648821],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"case_report","study_design_scores_codex":[0.005938748,0.003154436,0.0873157,0.001231224,0.001110088,0.02786032,0.0004270534,0.0001429412,0.5511103,0.0009213906,0.1717371,0.1490508],"study_design_scores_gemma":[0.009537932,0.002502343,0.001578286,0.00172867,0.0004218622,0.478606,0.0006138119,0.001035915,0.06099371,0.002126296,0.4393877,0.001467543],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9892781,0.0005051631,0.005827017,0.00004278895,0.002664027,0.0003280198,0.0001244682,0.00005076363,0.001179612],"genre_scores_gemma":[0.9815456,0.000009500784,0.005242079,0.00004683313,0.000258615,0.00000375495,0.00002039263,0.00005766829,0.01281552],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4901165,"threshold_uncertainty_score":0.8409199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02545662266720686,"score_gpt":0.3085413191325773,"score_spread":0.2830846964653704,"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."}}