{"id":"W4205916580","doi":"10.1109/bigdata52589.2021.9671738","title":"Predicting family physicians based on their practice using machine learning","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Conference on Big Data (Big Data)","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"NOSM University; Lakehead University","funders":"","keywords":"Machine learning; Rurality; Artificial intelligence; Computer science; Health care; Binary classification; Field (mathematics); Class (philosophy); Medicine; Rural area; Support vector machine; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001929343,0.000402217,0.0004404282,0.0002245458,0.001141521,0.000179324,0.002753369,0.0002778878,0.0008522271],"category_scores_gemma":[0.007772014,0.0003970173,0.00006187129,0.0004566374,0.0001184258,0.001027013,0.001564961,0.002507027,0.0007668087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003838853,"about_ca_system_score_gemma":0.00230118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005411548,"about_ca_topic_score_gemma":0.006775146,"domain_scores_codex":[0.9936801,0.001743369,0.001050479,0.001566616,0.001237889,0.0007216117],"domain_scores_gemma":[0.9910365,0.003225091,0.0008291221,0.003144573,0.001563333,0.0002013739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002199931,0.002315929,0.0825379,0.0006039341,0.0008121409,0.001078959,0.004633793,0.009483958,0.04512434,0.01036045,0.02388233,0.8169664],"study_design_scores_gemma":[0.0003422445,0.0001045962,0.0004282115,0.001679148,0.00004955089,0.00000746549,0.009975879,0.8686799,0.001108185,0.000191706,0.1170635,0.0003696345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3732041,0.0008507565,0.1437652,0.08481803,0.1022671,0.004423173,0.1419457,0.001142063,0.1475839],"genre_scores_gemma":[0.9511344,0.0005406893,0.002249314,0.009107364,0.005939366,0.0000446082,0.03005038,0.00009153417,0.0008423529],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8591959,"threshold_uncertainty_score":0.9998482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6762616049797713,"score_gpt":0.5199363785259139,"score_spread":0.1563252264538574,"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."}}