{"id":"W4379348142","doi":"10.1017/cjn.2023.136","title":"P.032 Using clinical MRI scans for research purposes: a preliminary feasibility study","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Neuroimaging; Neuropsychology; Operationalization; Medicine; Voxel-based morphometry; Voxel; Psychology; Medical physics; Cognition; Magnetic resonance imaging; Radiology; Psychiatry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.1052873,0.0007950281,0.001054091,0.001323273,0.001124532,0.002493073,0.001342318,0.001489183,0.01227257],"category_scores_gemma":[0.1543706,0.0009512106,0.002108627,0.002265913,0.001259812,0.002593036,0.002232194,0.001021209,0.002528647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001023055,"about_ca_system_score_gemma":0.00780398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003503011,"about_ca_topic_score_gemma":0.007237795,"domain_scores_codex":[0.9311112,0.05690564,0.00514175,0.001651736,0.004204047,0.0009856765],"domain_scores_gemma":[0.8066992,0.1559614,0.01126373,0.008861504,0.01500872,0.002205493],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.04263332,0.00957786,0.5418283,0.0358795,0.005445198,0.002604788,0.01218057,0.001583705,0.005738113,0.007069543,0.01510295,0.3203562],"study_design_scores_gemma":[0.02133812,0.07123919,0.7322724,0.0157941,0.01344152,0.004831785,0.01528439,0.008852336,0.005413973,0.01269264,0.09847104,0.0003684866],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.756977,0.01457567,0.06457151,0.01194316,0.0005884049,0.1047916,0.01079197,0.000372828,0.03538783],"genre_scores_gemma":[0.8016493,0.002941053,0.1016857,0.001815959,0.0001631406,0.08723814,0.002590269,0.00009209062,0.001824336],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1052873,"threshold_uncertainty_score":0.5568191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8986270334698269,"score_gpt":0.6195915877153549,"score_spread":0.2790354457544719,"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."}}