{"id":"W4238478970","doi":"10.31525/ct1-nct04162522","title":"Canadian Biomarker Integration Network for Depression (CAN-BIND) - Validation Study","year":2019,"lang":"en","type":"article","venue":"Case Medical Research","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Biomarker; Depression (economics); Computational biology; Psychology; Biology; Genetics; Economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.01174896,0.001027321,0.001349292,0.002639975,0.005853683,0.002772161,0.004040121,0.001302299,0.006901171],"category_scores_gemma":[0.03266035,0.0007843942,0.001626368,0.007134607,0.001266913,0.0009333382,0.003246073,0.002193761,0.001376736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04041843,"about_ca_system_score_gemma":0.0912678,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.9674004,"about_ca_topic_score_gemma":0.9782507,"domain_scores_codex":[0.9918064,0.001853223,0.0004595649,0.001182239,0.003204768,0.001493808],"domain_scores_gemma":[0.9739262,0.003142764,0.002214695,0.002867404,0.01489419,0.002954721],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004513553,0.0006619218,0.721348,0.0009242424,0.003083303,0.0004723081,0.0010241,0.002370863,0.0007162109,0.008500642,0.2095653,0.04681948],"study_design_scores_gemma":[0.001972551,0.0002644906,0.9083695,0.0007986263,0.003389127,0.0005609938,0.001233601,0.003301698,0.000755812,0.00229345,0.07691495,0.0001452226],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6227517,0.008104466,0.008987682,0.01408702,0.0008684595,0.004603392,0.2920072,0.0004392116,0.04815095],"genre_scores_gemma":[0.8546158,0.00284172,0.01171286,0.004536696,0.0002162469,0.002859436,0.111715,0.0002421583,0.01126003],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9674004,"threshold_uncertainty_score":0.2932576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04206719302159629,"score_gpt":0.3685513645322734,"score_spread":0.3264841715106771,"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."}}