{"id":"W2001130496","doi":"10.1016/j.neurobiolaging.2009.08.018","title":"Gigaxonin mutation analysis in patients with NIFID","year":2009,"lang":"en","type":"article","venue":"Neurobiology of Aging","topic":"Neurological diseases and metabolism","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre hospitalier de l'Université Laval; Université Laval","funders":"National Institute on Aging","keywords":"Frontotemporal dementia; Frontotemporal lobar degeneration; Pathology; Dementia; Mutation; Cytoplasmic inclusion; Biology; Cytoplasm; Disease; Gene; Molecular biology; Medicine; Genetics","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":[],"consensus_categories":[],"category_scores_codex":[0.00004296278,0.00008850427,0.0002068687,0.0002188029,0.00003026154,0.000006257106,0.0001248706,0.00003264749,0.00002313662],"category_scores_gemma":[0.00007996601,0.00006568724,0.00005814553,0.0006406083,0.00007855113,0.00007190798,0.00001675986,0.0001031567,0.000003995789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003750528,"about_ca_system_score_gemma":0.000007535177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004735056,"about_ca_topic_score_gemma":0.000001981475,"domain_scores_codex":[0.9990979,0.0001440303,0.0001734017,0.0003150843,0.00008057292,0.0001890168],"domain_scores_gemma":[0.9996135,0.00008941532,0.00009810425,0.0001404261,0.00001989441,0.00003861506],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003182271,0.0006823106,0.9276285,0.000008014157,0.00001833047,0.00005452889,0.0001092205,0.004242106,0.05936731,0.0017166,0.00002186259,0.005833014],"study_design_scores_gemma":[0.0005185265,0.0003420757,0.9832783,0.000002447681,0.00004732976,6.251112e-7,0.00000172976,0.00006304133,0.01542847,0.0002279063,0.00002594307,0.00006357377],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991617,0.00000877089,0.00003443315,0.0002339182,0.000047017,0.0001080985,0.000007936751,0.00002584627,0.0003722627],"genre_scores_gemma":[0.9979198,0.000006637903,0.00004754295,0.001992714,0.000007800819,0.000001817416,0.000006249643,0.000003397759,0.00001406921],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05564985,"threshold_uncertainty_score":0.2678648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009658211123817043,"score_gpt":0.2335364042874842,"score_spread":0.2238781931636672,"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."}}