{"id":"W4224926642","doi":"10.1016/j.jad.2022.04.122","title":"Depression screening using a non-verbal self-association task: A machine-learning based pilot study","year":2022,"lang":"en","type":"article","venue":"Journal of Affective Disorders","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Alberta Innovates; Mitacs; Canada Research Chairs; University of Alberta; Mental Health Foundation","keywords":"Beck Depression Inventory; Depression (economics); Receiver operating characteristic; Psychology; Major depressive disorder; Clinical psychology; Neuroimaging; Machine learning; Artificial intelligence; Psychiatry; Computer science; Anxiety; Mood","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":[0.002260882,0.0009388846,0.001100155,0.0004004074,0.0009822213,0.0005589419,0.0009228772,0.001027951,0.002161257],"category_scores_gemma":[0.003541019,0.0005049476,0.0006824863,0.0002793249,0.0007197912,0.0008010902,0.0004598046,0.001465691,0.001030617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004275309,"about_ca_system_score_gemma":0.0008925761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00455604,"about_ca_topic_score_gemma":0.005689658,"domain_scores_codex":[0.9992631,0.0003140561,0.00006575763,0.0001468957,0.0001208389,0.00008936047],"domain_scores_gemma":[0.9976116,0.001087498,0.0001219792,0.0002896093,0.0004606802,0.0004284977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"nonrandomized_trial","study_design_gemma":"observational","study_design_scores_codex":[0.05922322,0.4660362,0.2756614,0.0005234733,0.0007495742,0.001591489,0.007142545,0.003065183,0.05715059,0.0004152665,0.003035777,0.1254052],"study_design_scores_gemma":[0.01685804,0.2504897,0.7105035,0.00003645121,0.0005196476,0.0008675623,0.001618247,0.009843402,0.007189111,0.0004689832,0.001466114,0.0001392508],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979892,0.00001455885,0.000524828,0.00003706555,0.00001944238,0.0009929881,0.0001604115,0.00001531143,0.0002462698],"genre_scores_gemma":[0.9924027,0.0000755562,0.002875834,0.0001655968,0.00004264705,0.002414288,0.0008188304,0.0000152216,0.001189388],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00455604,"threshold_uncertainty_score":0.01195681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02032564330969194,"score_gpt":0.3460212985353601,"score_spread":0.3256956552256681,"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."}}