{"id":"W3186517501","doi":"10.1111/cid.13033","title":"Geometric accuracy of magnetic resonance imaging<scp>–</scp>derived virtual <scp>3‐dimensional</scp> bone surface models of the mandible in comparison to computed tomography and cone beam computed tomography<scp>:</scp> A porcine cadaver study","year":2021,"lang":"en","type":"article","venue":"Clinical Implant Dentistry and Related Research","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ludwig-Maximilians-Universität München; Technische Universität München","keywords":"Cone beam computed tomography; Magnetic resonance imaging; Nuclear medicine; Mandible (arthropod mouthpart); Tomography; Computed tomography; Materials science; Medicine; Biomedical engineering; Radiology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.003550404,0.0006099074,0.001721857,0.001596493,0.0005471298,0.0002982295,0.0009409317,0.000521814,0.00001708688],"category_scores_gemma":[0.003327563,0.0005299894,0.0005502024,0.009311421,0.001643995,0.0004266631,0.001790881,0.002696503,0.00002385011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003826745,"about_ca_system_score_gemma":0.0002029991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003726527,"about_ca_topic_score_gemma":0.0001093061,"domain_scores_codex":[0.9905491,0.001924153,0.002707772,0.001606651,0.001879901,0.001332465],"domain_scores_gemma":[0.9826206,0.01390001,0.0006925249,0.001114784,0.0009681116,0.0007039878],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001663495,0.005310202,0.934206,0.000410684,0.0007627215,0.0109456,0.001241945,0.001833194,0.01677601,0.0001569527,0.02597404,0.002216307],"study_design_scores_gemma":[0.007552424,0.000966458,0.9574412,0.0009795394,0.0002600298,0.00574144,0.005322822,0.01281961,0.007568702,0.0003891357,0.0007983254,0.0001603057],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9590493,0.03661009,0.0001358858,0.00006512731,0.001864558,0.001471329,0.0004642896,0.00007558974,0.0002638711],"genre_scores_gemma":[0.997094,0.000821298,0.0003445647,0.00008829305,0.00005961486,0.00002339546,0.0001621793,0.00006671401,0.001339889],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0380448,"threshold_uncertainty_score":0.9997151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05108495664148129,"score_gpt":0.3596025012307245,"score_spread":0.3085175445892432,"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."}}