{"id":"W4389732888","doi":"10.2196/48351","title":"Development of Risk Prediction Models for Severe Periodontitis in a Thai Population: Statistical and Machine Learning Approaches","year":2023,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Oral microbiology and periodontitis research","field":"Dentistry","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Research Council of Thailand; Mahidol University","keywords":"Medicine; Logistic regression; Periodontitis; Periodontology; Dentistry; Oral hygiene; Decision tree; Population; Clinical attachment loss; Gingival recession; Gold standard (test); Artificial intelligence; Computer science","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":[0.01235529,0.001502125,0.001143619,0.002536472,0.0005003273,0.001742135,0.001658391,0.0009638462,0.001676459],"category_scores_gemma":[0.02183853,0.0007057287,0.002943428,0.001124997,0.0004482722,0.001126652,0.001282024,0.002254141,0.0003661765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001168191,"about_ca_system_score_gemma":0.001787154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01449163,"about_ca_topic_score_gemma":0.008599439,"domain_scores_codex":[0.9965315,0.002412251,0.0002267155,0.0004627535,0.0002021714,0.0001646535],"domain_scores_gemma":[0.9773018,0.01898457,0.00156405,0.0003852693,0.001297637,0.0004666702],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001310951,0.001135905,0.4471364,0.0003889273,0.002419054,0.000892569,0.0007394465,0.4432728,0.0006994436,0.003056338,0.002941619,0.09600648],"study_design_scores_gemma":[0.00002161796,0.0001960859,0.01024625,0.00004254331,0.0001846792,0.0000716967,0.0001347814,0.9873183,0.00009779041,0.001450554,0.0002137746,0.00002186403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7691403,0.001967479,0.2222467,0.002391428,0.0001664829,0.0005044958,0.001839037,0.0005880475,0.001156017],"genre_scores_gemma":[0.9494624,0.0004996736,0.04721977,0.0001362919,0.00008718307,0.0004352501,0.001378043,0.00003182748,0.0007496527],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01449163,"threshold_uncertainty_score":0.06534183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1419930090949931,"score_gpt":0.3860468798189057,"score_spread":0.2440538707239127,"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."}}