{"id":"W6977455751","doi":"10.6084/m9.figshare.26724629","title":"Additional file 1 of Socio-demographic predictors of not having private dental insurance coverage: machine-learning algorithms may help identify the disadvantaged","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"","keywords":"Disadvantaged; Dental insurance; Health insurance; Data collection","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001253487,0.0009092063,0.0009870995,0.002107112,0.000718471,0.001281775,0.001516697,0.001597189,0.8650016],"category_scores_gemma":[0.0328705,0.0004152297,0.001097018,0.002904569,0.0002512237,0.001629539,0.0009178617,0.0009773052,0.1608089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007307324,"about_ca_system_score_gemma":0.00153042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01247176,"about_ca_topic_score_gemma":0.01973307,"domain_scores_codex":[0.9994956,0.000116511,0.00008085828,0.0001330278,0.0000766328,0.00009724949],"domain_scores_gemma":[0.979872,0.01503318,0.001457686,0.001049367,0.002001614,0.0005860619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0003276121,0.00019218,0.01070604,0.0011898,0.00005984448,0.00007151527,0.0000398415,0.0004951505,0.00003816709,0.0008036829,0.9736425,0.01243373],"study_design_scores_gemma":[0.01038823,0.0008502195,0.2107894,0.006301092,0.00069268,0.00127472,0.001366879,0.01218639,0.001032902,0.03071299,0.7240795,0.0003248353],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.0005810879,0.00002235766,0.0002459356,0.0001804741,0.00004021,0.00006828311,0.9971319,0.0001383231,0.001591474],"genre_scores_gemma":[0.03400838,0.000246358,0.005916968,0.001571451,0.0003422161,0.002536393,0.9320034,0.000611921,0.02276303],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8650016,"threshold_uncertainty_score":0.1925588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06569396339472788,"score_gpt":0.3611148316171526,"score_spread":0.2954208682224247,"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."}}