{"id":"W2883417149","doi":"10.1016/j.jalz.2018.06.2855","title":"Molecular imaging in dementia: Past, present, and future","year":2018,"lang":"en","type":"review","venue":"Alzheimer s & Dementia","topic":"Alzheimer's disease research and treatments","field":"Medicine","cited_by":88,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"National Institute on Aging","keywords":"Dementia; Molecular imaging; Disease; Neuroscience; Medicine; Neuroimaging; Modality (human–computer interaction); Clinical trial; Clinical Practice; Amyloid (mycology); Pathological; Pathology; Bioinformatics; Psychology; Computer science; Artificial intelligence; In vivo; Biology; Physical therapy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001257285,0.0009191297,0.001406562,0.003377327,0.0003730094,0.00152725,0.001000283,0.001632691,0.003467036],"category_scores_gemma":[0.00122866,0.0003296303,0.0006544169,0.003384111,0.00100147,0.002727946,0.0009432057,0.002276649,0.002053862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000785307,"about_ca_system_score_gemma":0.001589664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001571786,"about_ca_topic_score_gemma":0.002424673,"domain_scores_codex":[0.9997206,0.00007113921,0.00005184424,0.00004343501,0.00008189002,0.00003102668],"domain_scores_gemma":[0.9992929,0.0003570151,0.00009668404,0.00002562536,0.0001724573,0.00005529734],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007099374,0.00006364292,0.0002171795,0.01619071,0.00006246901,0.0001575884,0.0001154826,0.0002091166,0.0007445052,0.005665413,0.0229342,0.9535686],"study_design_scores_gemma":[0.00001425459,0.00009347634,0.0008957824,0.008797548,0.0001062439,0.001604978,0.0001745374,0.00009080521,0.0002796016,0.005029842,0.9828888,0.00002408272],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00004554093,0.9990044,0.00005492959,0.0003398257,0.0001255278,0.000002231567,0.000005831062,0.000003576217,0.000418088],"genre_scores_gemma":[0.0003398742,0.9989303,0.0001380486,0.0001698047,0.0001595706,0.000003497982,0.000008917762,0.000001062681,0.0002489589],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003467036,"threshold_uncertainty_score":0.01159835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03460695600750745,"score_gpt":0.3463455285505272,"score_spread":0.3117385725430198,"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."}}