{"id":"W2945395227","doi":"10.1017/s204579601900026x","title":"Subtyping psychological distress in the population: a semi-parametric network approach","year":2019,"lang":"en","type":"article","venue":"Epidemiology and Psychiatric Sciences","topic":"Mental Health Research Topics","field":"Psychology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek; ZonMw","keywords":"Centrality; Psychology; Distress; Anxiety; Population; Cluster analysis; Cluster (spacecraft); Clinical psychology; Psychiatry; Medicine; Artificial intelligence; Computer science; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01113798,0.0001513682,0.0003664919,0.0002329164,0.0004481972,0.00002120847,0.0006330114,0.000235231,0.0003765891],"category_scores_gemma":[0.0004396474,0.00009029824,0.00005930966,0.002181158,0.0004325763,0.00008108726,0.00006114713,0.0005953537,0.000119837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002095732,"about_ca_system_score_gemma":0.00001401576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004757395,"about_ca_topic_score_gemma":0.00003576861,"domain_scores_codex":[0.9948324,0.00281253,0.000557401,0.0007303674,0.0002024055,0.0008648794],"domain_scores_gemma":[0.9954684,0.00384451,0.0001895713,0.0003772863,0.00001104974,0.0001091355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004383822,0.0001028951,0.8819107,0.00002053836,0.000004145698,0.000001625366,0.0001676583,0.0002121289,3.808428e-8,0.1069096,0.003687984,0.006938763],"study_design_scores_gemma":[0.0003313011,0.0003365171,0.9655626,0.000008200561,0.000003978036,0.00007605926,0.0004736437,0.001331063,7.304259e-9,0.03034327,0.001425617,0.0001076954],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9284173,0.009262579,0.0003081701,0.006037079,0.001782014,0.0006620515,0.000003054635,0.0000257027,0.05350205],"genre_scores_gemma":[0.9924929,0.000225061,0.002997365,0.003424522,0.0004149712,0.00007458912,0.00001169195,0.000004524562,0.0003543394],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08365189,"threshold_uncertainty_score":0.4123387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.167157439509617,"score_gpt":0.4718792362273489,"score_spread":0.3047217967177319,"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."}}