{"id":"W2783066278","doi":"10.5539/ijsp.v7n2p1","title":"The Transmuted Weibull Regression Model: an Application to Type 2 Diabetes Mellitus Data","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mathematics; Weibull distribution; Statistics; Glycated hemoglobin; Estimator; Proportional hazards model; Covariate; Logistic regression; Type 2 Diabetes Mellitus; Medicine; Type 2 diabetes; Diabetes mellitus; Endocrinology","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":[0.01085297,0.0006495181,0.00119826,0.001683846,0.0005701947,0.001292736,0.00268067,0.001800844,0.002325301],"category_scores_gemma":[0.03023597,0.0003451708,0.001643284,0.002798303,0.000742267,0.001149288,0.001465656,0.002494168,0.0005036237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007948777,"about_ca_system_score_gemma":0.0008046547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006722404,"about_ca_topic_score_gemma":0.0036245,"domain_scores_codex":[0.9964851,0.00257668,0.00012518,0.0003799752,0.0002748283,0.0001581204],"domain_scores_gemma":[0.976182,0.01908479,0.001619153,0.001855482,0.0009326311,0.0003259595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007211538,0.0003290387,0.07183787,0.0003645626,0.0006705998,0.002212932,0.0008447421,0.7338551,0.001211803,0.08599022,0.003320234,0.09864166],"study_design_scores_gemma":[0.00003305259,0.0001826724,0.006032781,0.00003619705,0.00006834231,0.0004525755,0.0001598843,0.9587501,0.0002794786,0.03231502,0.001646399,0.00004351681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1975024,0.001434639,0.7963268,0.001468297,0.0001499097,0.0001637694,0.00142101,0.0004270997,0.001106137],"genre_scores_gemma":[0.8634338,0.001635996,0.129299,0.0002715403,0.0002220457,0.000310141,0.001505012,0.0001011431,0.003221459],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01085297,"threshold_uncertainty_score":0.05739671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0950120367992613,"score_gpt":0.4111380372309837,"score_spread":0.3161260004317224,"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."}}