{"id":"W7105855072","doi":"10.1109/taffc.2025.3634148","title":"Mind AI's Mind: A Clinically Aligned Explainable AI Pipeline for Depression Diagnosis via Large Language Models","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"China Scholarship Council","keywords":"Pipeline (software); Harm; Flagging; Skepticism; Field (mathematics); Dual (grammatical number)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.003195208,0.0009710329,0.001265513,0.0009792527,0.002769994,0.0005050225,0.001437029,0.0007426112,0.0001048033],"category_scores_gemma":[0.0003744709,0.001056441,0.0009109016,0.001815579,0.0001505743,0.0008298666,0.00007750493,0.001875688,0.00005983215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006544741,"about_ca_system_score_gemma":0.0006324662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005823282,"about_ca_topic_score_gemma":0.000367338,"domain_scores_codex":[0.9912661,0.001847956,0.001765455,0.002624999,0.0007306254,0.001764864],"domain_scores_gemma":[0.9889345,0.007370309,0.0006690949,0.001574776,0.0009964067,0.0004549258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004174924,0.001439775,0.0007460513,0.0005499486,0.0001826198,0.0000359496,0.006726669,0.3457278,0.0001871316,0.0001245022,0.000466856,0.6433952],"study_design_scores_gemma":[0.003667675,0.0009581922,0.0002520054,0.002140377,0.0002442792,0.00001817589,0.0004765822,0.9769445,0.01306853,0.0007654036,0.0006258436,0.0008384273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02663462,0.0007543097,0.9604609,0.003362153,0.004374182,0.003620971,0.0001527985,0.0002875674,0.0003524891],"genre_scores_gemma":[0.9624573,0.00004774349,0.03249531,0.003322626,0.0003439999,0.0004990451,0.00001492087,0.00009678473,0.0007222658],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9358227,"threshold_uncertainty_score":0.9991886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01972609599373583,"score_gpt":0.3559604921077318,"score_spread":0.336234396113996,"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."}}