{"id":"W3165332314","doi":"","title":"Do DTI features add value to clinical and SPECT imaging features for outcome prediction in Parkinson’s disease?","year":2021,"lang":"en","type":"article","venue":"","topic":"Parkinson's Disease Mechanisms and Treatments","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Diffusion MRI; Parkinson's disease; Feature selection; Artificial intelligence; Fractional anisotropy; Putamen; Computer science; Dopamine transporter; Disease; Machine learning; Pattern recognition (psychology); Medicine; Internal medicine; Magnetic resonance imaging; Radiology; Dopamine; Dopaminergic","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002201923,0.0007197747,0.0009167516,0.001596804,0.0002529351,0.0009965588,0.0004733489,0.0007825188,0.001308985],"category_scores_gemma":[0.004859119,0.000173268,0.0009355948,0.0008869341,0.0003938721,0.0006757765,0.0005246854,0.0005947891,0.0005415161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00025675,"about_ca_system_score_gemma":0.0003831577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001525154,"about_ca_topic_score_gemma":0.003211035,"domain_scores_codex":[0.9993345,0.0002779977,0.00006839623,0.0001717368,0.00008461704,0.00006275271],"domain_scores_gemma":[0.9977239,0.0012114,0.0004576718,0.0002265525,0.0002078319,0.0001727009],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002842865,0.0003598692,0.9051769,0.0002027617,0.0008518319,0.0001640978,0.00005864918,0.007011113,0.003910565,0.0001584779,0.002202638,0.07706019],"study_design_scores_gemma":[0.0001838143,0.001335576,0.8820348,0.0001250088,0.0006123211,0.0005633263,0.0001514587,0.1080817,0.00281086,0.001662174,0.002367707,0.00007131329],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9901613,0.001756739,0.003152054,0.0003970723,0.00003934557,0.00003484332,0.003579312,0.00007216426,0.000807169],"genre_scores_gemma":[0.9934173,0.0002000633,0.001843041,0.00005247011,0.00005473492,0.0000251708,0.004195942,0.00001072352,0.0002006138],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002201923,"threshold_uncertainty_score":0.01164508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03479687465928296,"score_gpt":0.3561117202441146,"score_spread":0.3213148455848317,"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."}}