{"id":"W4372403590","doi":"10.1101/2023.05.05.539590","title":"Predicting Parkinson’s disease progression using MRI-based white matter radiomic biomarker and machine learning: a reproducibility and replicability study","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Concordia University","funders":"Michael J. Fox Foundation for Parkinson's Research","keywords":"Artificial intelligence; Replicate; Robustness (evolution); Neuroimaging; Machine learning; Magnetic resonance imaging; Biomarker; Cohort; Parkinson's disease; Imaging biomarker; Reproducibility; Population; Medicine; Computer science; Feature selection; Disease; Internal medicine; Statistics; Radiology; Mathematics; Biology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.134169,0.001345632,0.001046927,0.001288019,0.000788276,0.002519288,0.001959024,0.001670116,0.001209861],"category_scores_gemma":[0.2229676,0.0006166741,0.003051101,0.001240425,0.002480128,0.001495006,0.001976532,0.001939822,0.000855747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005286164,"about_ca_system_score_gemma":0.001017313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001455112,"about_ca_topic_score_gemma":0.0008382713,"domain_scores_codex":[0.9223588,0.05547182,0.005576812,0.01087087,0.005207352,0.0005143488],"domain_scores_gemma":[0.6731724,0.1899121,0.01366339,0.1013352,0.0207412,0.001175685],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01355826,0.002148211,0.7136148,0.002165028,0.02277586,0.0008500734,0.002839876,0.06174855,0.02882979,0.00432128,0.005003874,0.1421444],"study_design_scores_gemma":[0.001612946,0.02261562,0.530464,0.001282254,0.01335035,0.00296213,0.001344287,0.3084906,0.07538263,0.01993093,0.02189795,0.0006663544],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7680161,0.004178795,0.219301,0.0007324365,0.0008591872,0.001078754,0.002227372,0.0007625132,0.002843915],"genre_scores_gemma":[0.9761613,0.0001846634,0.02070942,0.0001893948,0.0001357734,0.0004099778,0.001592704,0.000221025,0.000395703],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.865831,"threshold_uncertainty_score":0.7095621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07022890400720562,"score_gpt":0.3276404672216753,"score_spread":0.2574115632144697,"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."}}