{"id":"W4251528675","doi":"10.52547/koomesh.23.3.402","title":"Predictive factors of glycosylated hemoglobin using additive regression model","year":2021,"lang":"en","type":"article","venue":"Koomesh Journal","topic":"Liver Disease Diagnosis and Treatment","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hemoglobin; Regression; Regression analysis; Internal medicine; Mathematics; Statistics; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.005060594,0.002107381,0.001755533,0.003078416,0.0006065553,0.002438416,0.002379186,0.001071746,0.008902957],"category_scores_gemma":[0.01238991,0.0007588425,0.003113865,0.001983775,0.0003522925,0.0009243459,0.0009728131,0.002342903,0.001772134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007243114,"about_ca_system_score_gemma":0.001950175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02014987,"about_ca_topic_score_gemma":0.007430775,"domain_scores_codex":[0.9972166,0.001457166,0.0001357445,0.0005616701,0.0003043487,0.0003244419],"domain_scores_gemma":[0.9919726,0.00595959,0.0006353041,0.0002387397,0.0008628402,0.0003309771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0017347,0.001511551,0.6263642,0.0005828561,0.004246401,0.001516819,0.0005962163,0.2259823,0.0009392707,0.007185654,0.01481925,0.1145208],"study_design_scores_gemma":[0.00006006005,0.0002497655,0.02746154,0.0001165906,0.0006612762,0.0002375637,0.0001549196,0.9657987,0.0001742805,0.003523029,0.001511724,0.00005050348],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7110112,0.007201583,0.2614016,0.004395075,0.001024857,0.0006162581,0.00466205,0.00200688,0.007680571],"genre_scores_gemma":[0.9665259,0.001610383,0.02235113,0.0001751394,0.0003543586,0.0002900029,0.002561556,0.0001031508,0.006028312],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02014987,"threshold_uncertainty_score":0.04006517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03580202517973338,"score_gpt":0.3067999365114699,"score_spread":0.2709979113317365,"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."}}