{"id":"W2053017760","doi":"10.1902/jop.2015.140584","title":"Sensitivity and Specificity of Radiographic Methods for Predicting Insertion Torque of Dental Implants","year":2015,"lang":"en","type":"article","venue":"Journal of Periodontology","topic":"Dental Implant Techniques and Outcomes","field":"Dentistry","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Radiography; Medicine; Implant; Demographics; Dentistry; Alveolar ridge; Cortical bone; Cone beam computed tomography; Computed tomographic; Radiology; Orthodontics; Nuclear medicine; Computed tomography; Surgery; Pathology","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":[0.00148547,0.000100183,0.000555088,0.0002006933,0.00002922565,0.00001148497,0.00008832238,0.0001343764,0.00000596894],"category_scores_gemma":[0.0003114099,0.00008451189,0.0001765021,0.00008232665,0.000123708,0.0001576223,0.00004603382,0.0001384721,2.303068e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003658672,"about_ca_system_score_gemma":0.00003999364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002445669,"about_ca_topic_score_gemma":0.0006868661,"domain_scores_codex":[0.9987349,0.00027032,0.0006214956,0.00009966136,0.0001369459,0.0001366673],"domain_scores_gemma":[0.9985497,0.0003004926,0.0007268146,0.00009570636,0.0002314709,0.00009580796],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008170378,0.0001018763,0.8226718,0.00009934681,0.0001336055,0.000205474,0.0004188533,0.000002490333,0.1595839,0.0002646592,0.0003425888,0.01535831],"study_design_scores_gemma":[0.001989748,0.00111265,0.833903,0.00009101579,0.0001658894,0.04519793,0.001240353,0.000370215,0.1143093,0.0007137454,0.000761361,0.0001448346],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9739953,0.00064112,0.02407856,0.00002400339,0.00100342,0.0001223448,0.00004698194,0.000009892357,0.00007837117],"genre_scores_gemma":[0.9637918,0.00004578975,0.03602185,0.00001711502,0.00009677551,0.000001070174,0.000002852913,0.000008247784,0.00001450889],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04527465,"threshold_uncertainty_score":0.3446295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05533641440755277,"score_gpt":0.3841092859654179,"score_spread":0.3287728715578651,"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."}}