{"id":"W3136582211","doi":"10.1038/s41746-021-00414-7","title":"Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge","year":2021,"lang":"en","type":"article","venue":"npj Digital Medicine","topic":"Parkinson's Disease Mechanisms and Treatments","field":"Medicine","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Max Planck ETH Center for Learning Systems; Academy of Finland; University of New South Wales; National Institutes of Health; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; European Commission; Cohen Veterans Bioscience; National Center for Advancing Translational Sciences; Agence Nationale de la Recherche; Georgia Clinical and Translational Science Alliance; University of Rochester; Michael J. Fox Foundation for Parkinson's Research; Robert Wood Johnson Foundation; American Parkinson Disease Association; National Institute of General Medical Sciences; Translational Cancer Research Network; Alfred Kordelinin Säätiö; Canadian Institutes of Health Research; National Science Foundation","keywords":"Dyskinesia; Crowdsourcing; Parkinson's disease; Disease; Physical medicine and rehabilitation; Biomarker; Medicine; Motor symptoms; Benchmark (surveying); Computer science; Internal medicine; World Wide Web","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.007290869,0.001793012,0.00150269,0.002224723,0.0009919244,0.00183569,0.001491568,0.001739846,0.00144354],"category_scores_gemma":[0.0158635,0.0002437736,0.0008807224,0.001756915,0.0009310684,0.001232576,0.003301319,0.001327335,0.001500534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000724476,"about_ca_system_score_gemma":0.001113783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008950361,"about_ca_topic_score_gemma":0.01068531,"domain_scores_codex":[0.99484,0.002761635,0.0002107096,0.0009346002,0.001076148,0.0001769908],"domain_scores_gemma":[0.990805,0.00442651,0.0007441752,0.002033446,0.001389663,0.0006010495],"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":[0.001962626,0.001117624,0.1728693,0.00233142,0.001925253,0.0007340934,0.001737051,0.03771397,0.009158663,0.004291457,0.08730576,0.6788528],"study_design_scores_gemma":[0.0008427532,0.001963241,0.2424051,0.00161855,0.0009277714,0.001565399,0.005758629,0.4781535,0.01533066,0.05845721,0.1924754,0.0005017135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7225272,0.02200938,0.1550593,0.01621553,0.004414591,0.002870943,0.03189142,0.00643722,0.03857447],"genre_scores_gemma":[0.9233272,0.001728415,0.0518009,0.002287988,0.0007137774,0.0006317701,0.01340746,0.0002275084,0.005874859],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008950361,"threshold_uncertainty_score":0.03855824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03138687479558548,"score_gpt":0.2793338662257316,"score_spread":0.2479469914301461,"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."}}