{"id":"W4205916785","doi":"10.1017/cjn.2021.443","title":"P.167 Application of the Anatomical Fiducials Framework to a Clinical Dataset of Patients with Parkinson’s Disease","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Fiducial marker; Bonferroni correction; Pairwise comparison; Medicine; Wilcoxon signed-rank test; Artificial intelligence; Magnetic resonance imaging; Pattern recognition (psychology); Computer science; Nuclear medicine; Radiology; Mathematics; Statistics; Internal medicine; Mann–Whitney U test","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002346348,0.0001805127,0.0004685341,0.0002967001,0.0005214365,0.0001363102,0.001555504,0.0001026009,0.00006914369],"category_scores_gemma":[0.004191636,0.0001014121,0.0001962106,0.001587045,0.003350161,0.000271052,0.00006675538,0.0007905398,9.572888e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006318314,"about_ca_system_score_gemma":0.001587664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002290769,"about_ca_topic_score_gemma":0.006289257,"domain_scores_codex":[0.9966832,0.0006409278,0.0009949956,0.0003273052,0.0007794419,0.0005740722],"domain_scores_gemma":[0.9965852,0.0006021321,0.000484568,0.000244998,0.000442636,0.001640419],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001965384,0.00003427532,0.9875352,0.000009033577,0.00001694373,0.0002092482,0.00005628779,0.007959973,0.00000794131,0.0001678008,0.000942804,0.00304078],"study_design_scores_gemma":[0.000188019,0.003452413,0.9786134,0.00007634494,0.00008804723,0.0002475953,0.00005574086,0.00545497,0.00008884532,0.00403215,0.00751637,0.0001861242],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959578,0.0003466175,0.0008066357,0.002107161,0.0004601254,0.00009083658,0.0001524596,0.000006698315,0.00007170314],"genre_scores_gemma":[0.9945585,0.00009869855,0.003456405,0.001742926,0.000131099,0.000001567873,0.000002022118,0.00000708029,0.000001702344],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008921878,"threshold_uncertainty_score":0.9993622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02226714935044672,"score_gpt":0.2823281059339519,"score_spread":0.2600609565835051,"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."}}