{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00508511,0.00004542308,0.000163268,0.0006327889,0.00003538666,0.0000176492,0.0002664878,0.00005396895,0.000252981],"category_scores_gemma":[0.004535984,0.00003308949,0.00003363349,0.0004643839,0.0001720335,0.00009647726,0.00006556419,0.0006842829,0.000002604538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001777176,"about_ca_system_score_gemma":0.0004498787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001824654,"about_ca_topic_score_gemma":0.001681454,"domain_scores_codex":[0.9961883,0.0005623426,0.0006883623,0.00007230362,0.002320635,0.0001680842],"domain_scores_gemma":[0.9969391,0.002042523,0.0002124495,0.00004445619,0.0006453729,0.0001161265],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002762641,0.0003590441,0.8959765,0.0001260304,0.00005450663,0.0004976841,0.001058591,0.001893881,0.0000163215,0.01024532,0.007805921,0.08168991],"study_design_scores_gemma":[0.00121059,0.000633239,0.8815559,0.0004625052,0.000003101956,0.000139372,0.0009709189,0.09512574,0.000008535927,0.01970968,0.0001525672,0.00002784534],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932618,0.000316838,0.004524593,0.0005754523,0.0002697773,0.000125482,0.0007651018,0.000002829599,0.0001581686],"genre_scores_gemma":[0.9954375,0.004167871,0.0001592208,0.0000311142,0.00004488914,0.000006822063,0.0001250295,0.00000530338,0.00002224257],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09323186,"threshold_uncertainty_score":0.5430324,"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."}}