{"id":"W4210606729","doi":"10.3390/jpm12020217","title":"Systemic Periodontal Risk Score Using an Innovative Machine Learning Strategy: An Observational Study","year":2022,"lang":"en","type":"article","venue":"Journal of Personalized Medicine","topic":"Oral microbiology and periodontitis research","field":"Dentistry","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre","funders":"Agence Nationale de la Recherche","keywords":"Periodontitis; Medicine; Observational study; Periodontium; Body mass index; Framingham Risk Score; Algorithm; Machine learning; Disease; Dentistry; Internal medicine; Computer science","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003736407,0.0002283938,0.0006820182,0.0004058397,0.0009344046,0.00005126018,0.0004725336,0.0000797758,0.007157484],"category_scores_gemma":[0.0003861718,0.0001824855,0.00009934191,0.000732189,0.0002605053,0.0004942338,0.0001231913,0.002017587,0.000005904147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003498106,"about_ca_system_score_gemma":0.0005162123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001221576,"about_ca_topic_score_gemma":0.0002706595,"domain_scores_codex":[0.9954144,0.002161264,0.0008748934,0.0002993388,0.0009294015,0.0003207461],"domain_scores_gemma":[0.9977825,0.0001313462,0.0009142843,0.0001879932,0.0007844375,0.0001994464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001634465,0.000741284,0.8859147,0.00003638566,0.0003443614,0.001550536,0.01162447,0.001588109,0.09528207,0.00007625463,0.0002025535,0.001004796],"study_design_scores_gemma":[0.02234683,0.02671462,0.7232975,0.0003575445,0.0004942744,0.02648116,0.1845717,0.009162409,0.000168775,0.0001035853,0.005570877,0.000730779],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996132,0.002347321,0.0001548569,0.00009925996,0.0007472094,0.0003101005,0.0001270947,0.00002088502,0.00006129297],"genre_scores_gemma":[0.9978444,0.00003963863,0.0002063924,0.0001066987,0.0008000651,0.00000806553,0.0001576241,0.00003383815,0.0008032878],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1729472,"threshold_uncertainty_score":0.9937501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2243571435491296,"score_gpt":0.4156900655663661,"score_spread":0.1913329220172365,"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."}}