{"id":"W4408842508","doi":"10.15173/mumj.v21i1.3657","title":"Analysis of temporal saccade prediction in Parkinson’s Disease using video-based eye tracking","year":2025,"lang":"en","type":"article","venue":"McMaster University Medical Journal","topic":"Parkinson's Disease Mechanisms and Treatments","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Kingston Health Sciences Centre","funders":"","keywords":"Saccade; Parkinson's disease; Eye tracking; Computer science; Eye movement; Tracking (education); Artificial intelligence; Physical medicine and rehabilitation; Computer vision; Psychology; Medicine; Disease; Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000369935,0.0001425687,0.0004452909,0.001282753,0.0001335612,0.00001683419,0.0001433624,0.000121626,0.001660326],"category_scores_gemma":[0.0001167966,0.0001325826,0.0003323593,0.00115798,0.00008150547,0.0001577397,0.00004685116,0.0003397637,0.000002027335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004305322,"about_ca_system_score_gemma":0.0007244353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009690975,"about_ca_topic_score_gemma":0.00005489131,"domain_scores_codex":[0.9983524,0.0001550802,0.0003321365,0.0002475283,0.0006704738,0.0002423911],"domain_scores_gemma":[0.9989712,0.00005031204,0.0001538568,0.0001470506,0.00009886854,0.0005787013],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001357688,0.0006703489,0.9826502,0.00005911669,0.001040089,0.002662155,0.00009915185,0.0007269147,0.0001387909,0.00002156969,0.00007734718,0.01049664],"study_design_scores_gemma":[0.005028742,0.00007162474,0.8834035,0.0006500574,0.004059562,0.000009703865,0.0003332385,0.09767159,0.00009686977,0.00002712889,0.008560341,0.0000875994],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9741879,0.0001864915,0.02398188,0.0007805054,0.0001504086,0.000133152,0.00004195137,0.00001963337,0.0005181485],"genre_scores_gemma":[0.9985837,0.00003440475,0.0005766273,0.0002983405,0.00004363429,3.077262e-7,0.00002885248,0.000006745894,0.000427422],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09924665,"threshold_uncertainty_score":0.9992523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02045789534175287,"score_gpt":0.2789943097409537,"score_spread":0.2585364143992009,"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."}}