{"id":"W2957457613","doi":"10.11919/j.issn.1002-0829.217031","title":"Inconsistency Between Univariate and Multiple Logistic Regressions.","year":2017,"lang":"en","type":"article","venue":"PubMed","topic":"Optimism, Hope, and Well-being","field":"Psychology","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Logistic regression; Univariate; Observational study; Covariate; Statistics; Logistic model tree; Regression analysis; Cross-sectional regression; Binomial regression; Regression diagnostic; Factor regression model; Univariate analysis; Econometrics; Medicine; Mathematics; Bayesian multivariate linear regression; Multivariate statistics; Multivariate analysis; Proper linear model","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":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2900655,0.001548947,0.002623932,0.005617702,0.001167823,0.004284278,0.003922004,0.002124284,0.004892068],"category_scores_gemma":[0.6929402,0.001068732,0.00351683,0.008351919,0.004475188,0.007754979,0.004712651,0.005212584,0.001216976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002646189,"about_ca_system_score_gemma":0.002232354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002030721,"about_ca_topic_score_gemma":0.002123046,"domain_scores_codex":[0.5714658,0.3249203,0.03686529,0.03020194,0.0352325,0.001314115],"domain_scores_gemma":[0.1942111,0.7292823,0.03322173,0.02317472,0.01936516,0.0007449554],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.003546519,0.0002306518,0.1973515,0.01336797,0.0162448,0.002703565,0.007670715,0.006560967,0.0009439006,0.2025436,0.05112339,0.4977123],"study_design_scores_gemma":[0.0005914262,0.00058952,0.09123101,0.008346653,0.006041171,0.009783146,0.004295954,0.05238798,0.003601985,0.7159955,0.106424,0.0007117394],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04349245,0.06150532,0.8379683,0.02683097,0.007234334,0.001728004,0.003599569,0.001388302,0.01625272],"genre_scores_gemma":[0.6345484,0.01392599,0.3228175,0.01206361,0.004633999,0.003035814,0.002848811,0.00113109,0.00499484],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7099345,"threshold_uncertainty_score":0.8754756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.094560864028491,"score_gpt":0.3200840468283271,"score_spread":0.2255231827998361,"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."}}