{"id":"W2021106518","doi":"10.1002/cjs.11136","title":"A functional marked point process model for lupus data","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Systemic Lupus Erythematosus Research","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Arthritis Society; McGill University Health Centre; McGill University","funders":"","keywords":"Point process; Systemic lupus erythematosus; Functional data analysis; Point (geometry); Process (computing); Statistics; Statistical model; Flare; Function (biology); Computer science; Econometrics; Medicine; Mathematics; Internal medicine; Engineering; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.02679901,0.001323957,0.002143662,0.002361381,0.0006907716,0.002615396,0.003873466,0.003106741,0.004704899],"category_scores_gemma":[0.05449364,0.0008860349,0.002522265,0.002063586,0.00244334,0.002995788,0.001640363,0.003270132,0.0008532574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002243289,"about_ca_system_score_gemma":0.001696921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01457426,"about_ca_topic_score_gemma":0.008995587,"domain_scores_codex":[0.9919288,0.004996994,0.0003370135,0.00145049,0.0008156352,0.0004710322],"domain_scores_gemma":[0.9424302,0.04713643,0.003545885,0.002682633,0.003545126,0.0006597862],"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":[0.0003842612,0.000110346,0.01307456,0.0002344403,0.0002858252,0.000489753,0.0005031567,0.7960656,0.001186692,0.1590157,0.001849283,0.02680042],"study_design_scores_gemma":[0.00003277788,0.0001109166,0.001260583,0.00001931444,0.00002959408,0.00006412654,0.00003885108,0.9639039,0.0001672054,0.03369996,0.0006396548,0.00003321259],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0653201,0.0002921087,0.9308174,0.0008392808,0.00007047974,0.0002242795,0.0009183463,0.0003774803,0.00114067],"genre_scores_gemma":[0.8739675,0.0004405147,0.1157503,0.0002696974,0.0001592929,0.0009054236,0.002119357,0.00009937576,0.006288543],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02679901,"threshold_uncertainty_score":0.1417284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1533730913434087,"score_gpt":0.3353438376459375,"score_spread":0.1819707463025288,"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."}}