{"id":"W4406154079","doi":"10.1186/s12903-025-05419-2","title":"Early childhood caries risk prediction using machine learning approaches in Bangladesh","year":2025,"lang":"en","type":"article","venue":"BMC Oral Health","topic":"Dental Health and Care Utilization","field":"Dentistry","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Universitetet i Bergen","keywords":"Medicine; Machine learning; Artificial intelligence; Random forest; Interpretability; AdaBoost; Early childhood caries; Receiver operating characteristic; Feature selection; Support vector machine; Gradient boosting; Dentistry; Oral health; 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":[],"consensus_categories":[],"category_scores_codex":[0.0006181087,0.0001502778,0.0002589863,0.0002277514,0.0003484113,0.00006223525,0.00009944132,0.0001293558,0.00002746194],"category_scores_gemma":[0.0001109473,0.0001568967,0.00005750576,0.0005870772,0.00003606831,0.0001925285,0.00005691692,0.0004503083,0.00002482051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003617653,"about_ca_system_score_gemma":0.0003922884,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01674494,"about_ca_topic_score_gemma":0.07667607,"domain_scores_codex":[0.9981148,0.0004344806,0.0004892366,0.0003276243,0.0002245235,0.0004093913],"domain_scores_gemma":[0.9994295,0.00005487539,0.0001873221,0.0001751589,0.0000304735,0.0001227082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006553043,0.00008927943,0.9847583,0.0002853206,0.000005709326,0.000002380847,0.0002899672,0.001019591,0.000001455124,0.0004668519,0.00008075594,0.01293489],"study_design_scores_gemma":[0.0008957842,0.0001058278,0.9616917,0.000153145,0.00001308482,0.0000103011,0.0006542168,0.03556069,0.00002010741,0.0002670465,0.0005350991,0.00009296255],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894205,0.002273142,0.006156264,0.00001497206,0.0007737272,0.0004944873,0.00008863968,0.0001541572,0.000624144],"genre_scores_gemma":[0.9976417,0.00007964065,0.001178101,0.0001690875,0.00008940853,0.00001731433,0.0002056734,0.00002000529,0.0005990295],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05993113,"threshold_uncertainty_score":0.9898027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05345303949449401,"score_gpt":0.3216324316114507,"score_spread":0.2681793921169567,"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."}}