{"id":"W2794103889","doi":"10.1016/j.jns.2018.02.022","title":"A diagnostic decision tree for adult cerebellar ataxia based on pontine magnetic resonance imaging","year":2018,"lang":"en","type":"article","venue":"Journal of the Neurological Sciences","topic":"Genetic Neurodegenerative Diseases","field":"Neuroscience","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Japan Society for the Promotion of Science; Ministry of Health, British Columbia","keywords":"Spinocerebellar ataxia; Pons; Magnetic resonance imaging; Differential diagnosis; Atrophy; Ataxia; Cerebellum; Cerebellar ataxia; Medicine; Pathology; Hyperintensity; Neuroscience; Anatomy; Psychology; Radiology; Internal medicine; Disease","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":[0.001541728,0.0007659201,0.001276375,0.002309412,0.0008567852,0.001395522,0.001165759,0.001336449,0.00296337],"category_scores_gemma":[0.005264927,0.0003969869,0.001128099,0.000657319,0.0002678983,0.0008302266,0.0007876637,0.001135829,0.0006806983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008279383,"about_ca_system_score_gemma":0.002561764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004416292,"about_ca_topic_score_gemma":0.006241626,"domain_scores_codex":[0.9993556,0.0001449624,0.0001155926,0.0001715483,0.0001272939,0.00008502475],"domain_scores_gemma":[0.9958168,0.003082451,0.0001868961,0.00007507361,0.0005323681,0.0003064097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001772428,0.0009928532,0.07148896,0.0007452889,0.0004447737,0.004414611,0.0004178261,0.2565143,0.007901635,0.00484037,0.03412861,0.6163383],"study_design_scores_gemma":[0.0001562131,0.000404544,0.006387921,0.000190802,0.0003484957,0.001739189,0.0001664813,0.9701403,0.003204557,0.01261992,0.004590361,0.00005129401],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2986893,0.002369656,0.6735406,0.005284165,0.0003610835,0.001491283,0.0084827,0.003990949,0.005790158],"genre_scores_gemma":[0.628152,0.000666137,0.3588074,0.0006747366,0.0002162131,0.0004953002,0.009390984,0.0001258255,0.001471436],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004416292,"threshold_uncertainty_score":0.009913445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02581249375146252,"score_gpt":0.280072820208307,"score_spread":0.2542603264568445,"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."}}