{"id":"W4407832587","doi":"10.1371/journal.pone.0317566","title":"Predicting Parkinson’s disease trajectory using clinical and functional MRI features: A reproduction and replication study","year":2025,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; McGill University","funders":"National Institute of General Medical Sciences; Région Bretagne; Mitacs; Agence Nationale de la Recherche; Michael J. Fox Foundation for Parkinson's Research","keywords":"Neuroimaging; Alzheimer's Disease Neuroimaging Initiative; Context (archaeology); Artificial intelligence; Computer science; Feature selection; Disease; Machine learning; Medicine; Cohort; Bioinformatics; Neuroscience; Psychology; Dementia; Pathology; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02515389,0.002036808,0.001431978,0.0006794297,0.0008118644,0.001614585,0.002006238,0.001569249,0.003034248],"category_scores_gemma":[0.07770919,0.0007539967,0.003883593,0.0006899169,0.001258388,0.001262782,0.001525836,0.002121201,0.003053829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004905838,"about_ca_system_score_gemma":0.0008159536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004716347,"about_ca_topic_score_gemma":0.00314593,"domain_scores_codex":[0.9928629,0.00356921,0.0006580847,0.001971517,0.0007406045,0.0001978236],"domain_scores_gemma":[0.9428251,0.01646272,0.002119741,0.03330681,0.004636082,0.0006494988],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.04567114,0.01480585,0.4112802,0.005364995,0.02213428,0.003666837,0.006884809,0.02670502,0.06787287,0.002513602,0.06900382,0.3240965],"study_design_scores_gemma":[0.01575866,0.0383934,0.6879811,0.001024956,0.02268787,0.007714537,0.002393965,0.1004591,0.04647779,0.0114301,0.06459773,0.001080793],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8994433,0.002302218,0.06573758,0.00092953,0.001266611,0.003291371,0.02160016,0.003043921,0.002385338],"genre_scores_gemma":[0.9173413,0.0003626601,0.0438918,0.0005671779,0.0003673587,0.003179447,0.03124418,0.0008951151,0.002151017],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9748461,"threshold_uncertainty_score":0.1330281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1376642455069903,"score_gpt":0.3229178319193365,"score_spread":0.1852535864123462,"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."}}