{"id":"W2041490883","doi":"10.1002/sim.6484","title":"A flexible mixed‐effect negative binomial regression model for detecting unusual increases in MRI lesion counts in individual multiple sclerosis patients","year":2015,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Multiple Sclerosis Society of Canada","keywords":"Negative binomial distribution; Random effects model; Dirichlet process; Dirichlet distribution; Statistics; Bayesian probability; Parametric statistics; Mixed model; Conditional probability distribution; Mathematics; Computer science; Medicine; Poisson distribution; Meta-analysis; Pathology","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.02653213,0.00169054,0.003462359,0.002522753,0.0005382592,0.002086412,0.006114386,0.0026829,0.003374527],"category_scores_gemma":[0.03958813,0.001470547,0.004067017,0.001882681,0.001648364,0.002015886,0.00183084,0.003276894,0.0007303871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001408653,"about_ca_system_score_gemma":0.001822965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008092685,"about_ca_topic_score_gemma":0.006785471,"domain_scores_codex":[0.9884426,0.008419648,0.0003281021,0.001580247,0.000839528,0.0003898284],"domain_scores_gemma":[0.9756492,0.01994296,0.001886133,0.001128065,0.00103057,0.0003629556],"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":[0.0009863606,0.0002110852,0.01400929,0.0005447146,0.001801142,0.001000446,0.0005001744,0.7938787,0.002184466,0.10384,0.003086271,0.07795738],"study_design_scores_gemma":[0.000155825,0.0001179512,0.001189901,0.00005327777,0.0003056281,0.0001470023,0.00001894655,0.9725279,0.0002933968,0.02338785,0.001753144,0.00004918534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01786989,0.0009115951,0.9783521,0.0009327249,0.0001181995,0.0002028861,0.0004775144,0.0003731832,0.0007619692],"genre_scores_gemma":[0.5150687,0.001740033,0.4710929,0.00118016,0.0003744192,0.002189724,0.001521551,0.0002264971,0.006606097],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02653213,"threshold_uncertainty_score":0.140317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1134339853815672,"score_gpt":0.3561553831504783,"score_spread":0.2427213977689112,"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."}}