{"id":"W4400118107","doi":"10.1111/cid.13357","title":"Artificial intelligence and mixed reality for dental implant planning: A technical note","year":2024,"lang":"en","type":"article","venue":"Clinical Implant Dentistry and Related Research","topic":"Dental Implant Techniques and Outcomes","field":"Dentistry","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Dental implant; Dentistry; Mixed reality; Computer science; Implant; Medicine; Orthodontics; Virtual reality; Artificial intelligence; Surgery","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004043856,0.0008053922,0.0004574966,0.001693453,0.0005023046,0.00304535,0.001579138,0.001616764,0.005048893],"category_scores_gemma":[0.003938169,0.0005241337,0.0008715991,0.0007932096,0.001948621,0.002095297,0.002775616,0.001730101,0.00244819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004291667,"about_ca_system_score_gemma":0.001389887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003967768,"about_ca_topic_score_gemma":0.0004751681,"domain_scores_codex":[0.996655,0.001193139,0.0002872437,0.0003765104,0.001390442,0.00009761239],"domain_scores_gemma":[0.9979252,0.000922895,0.0001040998,0.0005574922,0.0003997174,0.00009064141],"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":[0.0003012072,0.0001948432,0.001482847,0.0009632161,0.00008442946,0.0007700049,0.0008416151,0.006048627,0.05259724,0.05057283,0.004377647,0.8817655],"study_design_scores_gemma":[0.000196446,0.003194223,0.01030854,0.001349526,0.0003494752,0.02215065,0.0008245799,0.1189092,0.1743819,0.1144649,0.5531859,0.0006846583],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.00453251,0.003709825,0.9820015,0.0008175065,0.0003456704,0.0003953167,0.00007737527,0.0005902221,0.007529969],"genre_scores_gemma":[0.04067164,0.003693522,0.9479516,0.0002632207,0.0003047691,0.0008779496,0.000219136,0.0001849514,0.005833179],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.005048893,"threshold_uncertainty_score":0.02138627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2840663127335391,"score_gpt":0.5386241539796993,"score_spread":0.2545578412461603,"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."}}