{"id":"W2561218513","doi":"","title":"Сравнительная характеристика скрининговых шкал для определения когнитивных нарушений","year":2015,"lang":"ru","type":"article","venue":"Международный неврологический журнал","topic":"Neurological Disorders and Treatments","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Montreal Cognitive Assessment; Test (biology); Neuropsychology; Cognition; Mini–Mental State Examination; Neuropsychological test; Cog; Cognitive impairment; Psychology; Medicine; Audiology; Psychiatry; Computer science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00112249,0.0003670295,0.0003332794,0.00261286,0.0007911364,0.001969893,0.0003642041,0.0004094541,0.009465028],"category_scores_gemma":[0.002412172,0.0003539471,0.000361019,0.002319153,0.0009923294,0.0007493257,0.0006514085,0.0007972551,0.004353734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008389466,"about_ca_system_score_gemma":0.002199347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003341495,"about_ca_topic_score_gemma":0.006924111,"domain_scores_codex":[0.9988419,0.0002253046,0.00008269215,0.0001715434,0.0005907969,0.00008780441],"domain_scores_gemma":[0.9990957,0.0002873716,0.0002092047,0.0001068621,0.000234947,0.00006588508],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002292063,0.0001255897,0.008476717,0.0007580039,0.00007321914,0.002040185,0.002895115,0.001095716,0.0333479,0.063761,0.004938472,0.8822588],"study_design_scores_gemma":[0.0001122991,0.0006557399,0.05953684,0.0005565253,0.0003082298,0.01442819,0.002994976,0.002079559,0.04223223,0.0519,0.8249356,0.0002599036],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3438869,0.09197801,0.1411459,0.004522683,0.002039857,0.000800366,0.001477487,0.0006211422,0.4135276],"genre_scores_gemma":[0.779223,0.03266591,0.1455839,0.0002470473,0.0005094652,0.0005411079,0.0004683175,0.0001841686,0.04057717],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9988775,"threshold_uncertainty_score":0.03166372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1093740828075985,"score_gpt":0.3030233535358751,"score_spread":0.1936492707282766,"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."}}