{"id":"W3023893970","doi":"10.3390/jpm10020031","title":"Application of Machine Learning Technique to Distinguish Parkinson’s Disease Dementia and Alzheimer’s Dementia: Predictive Power of Parkinson’s Disease-Related Non-Motor Symptoms and Neuropsychological Profile","year":2020,"lang":"en","type":"article","venue":"Journal of Personalized Medicine","topic":"Parkinson's Disease Mechanisms and Treatments","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Research Foundation of Korea","keywords":"Dementia; Parkinson's disease; Neuropsychology; Disease; Physical medicine and rehabilitation; Cognition; REM sleep behavior disorder; Psychology; Medicine; Psychiatry; Pathology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004434995,0.0003255695,0.0008712801,0.000222257,0.00008683407,0.000008694775,0.0001367141,0.00009093788,0.0002294717],"category_scores_gemma":[0.0006960863,0.0002369195,0.0001756158,0.0003133877,0.0002757893,0.0001009838,0.00009007566,0.000355334,0.000001617509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003440113,"about_ca_system_score_gemma":0.0001209529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001982308,"about_ca_topic_score_gemma":5.206256e-7,"domain_scores_codex":[0.9974911,0.0001529724,0.0008917588,0.0004712719,0.0007289666,0.0002639485],"domain_scores_gemma":[0.9971123,0.00009824939,0.0008631332,0.0001891978,0.000359903,0.001377194],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0258582,0.001704532,0.8770054,0.0007979642,0.006314513,0.001118823,0.002856036,0.00001860742,0.07247801,0.0001658578,0.001072347,0.0106097],"study_design_scores_gemma":[0.0102304,0.007014271,0.9466869,0.001054675,0.009155505,0.0001210844,0.0003451162,0.003743835,0.0005840095,0.0001597361,0.02064347,0.0002609497],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9510673,0.02924899,0.01229156,0.004611344,0.0001513574,0.00217289,0.0002165096,0.00005003122,0.0001900563],"genre_scores_gemma":[0.9963434,0.0007899667,0.001962153,0.0005479223,0.0001527365,0.00008841334,0.00005466774,0.00004176361,0.00001900901],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.071894,"threshold_uncertainty_score":0.9661298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01548000889274344,"score_gpt":0.281130242953555,"score_spread":0.2656502340608116,"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."}}