{"id":"W4378806686","doi":"10.6000/1929-6029.2023.12.07","title":"Impact of Machine Learning and Prediction Models in the Diagnosis of Oral Health Conditions","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Dental Health and Care Utilization","field":"Dentistry","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Machine learning; Computer science; Bootstrapping (finance); Artificial intelligence; Predictive modelling; Oral health; Task (project management); Calibration; Data mining; Medicine; Statistics; Econometrics; Mathematics; Dentistry","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":[0.02523044,0.001119232,0.002121633,0.00621851,0.0004706711,0.0039581,0.001208669,0.001252456,0.002942892],"category_scores_gemma":[0.08549239,0.000319775,0.002932445,0.006371294,0.0007122468,0.003258103,0.001448175,0.002169517,0.0007570526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001568159,"about_ca_system_score_gemma":0.002767829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006041515,"about_ca_topic_score_gemma":0.003810993,"domain_scores_codex":[0.9872416,0.007650942,0.001171195,0.001037934,0.002662931,0.0002353297],"domain_scores_gemma":[0.8882456,0.1025061,0.002980464,0.001411252,0.004435867,0.0004207627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008896726,0.0004238843,0.1449406,0.0179539,0.004134777,0.0004471985,0.0004368213,0.06055895,0.0005246496,0.01558232,0.01665269,0.7374545],"study_design_scores_gemma":[0.0003372043,0.00208137,0.1359885,0.03692096,0.009444805,0.001633167,0.001652185,0.585765,0.003737805,0.1306122,0.0913561,0.0004706392],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.1038756,0.6739144,0.1523667,0.03558479,0.003773214,0.0005690128,0.009606392,0.0007317178,0.01957818],"genre_scores_gemma":[0.7298196,0.1967953,0.06093254,0.002049334,0.002655011,0.0004040822,0.005968833,0.00008767109,0.00128772],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.02523044,"threshold_uncertainty_score":0.1334329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1292111344971905,"score_gpt":0.5502154857784424,"score_spread":0.4210043512812518,"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."}}