{"id":"W3212409089","doi":"10.1186/s13195-021-00910-8","title":"Boosting the diagnostic power of amyloid-β PET using a data-driven spatially informed classifier for decision support","year":2021,"lang":"en","type":"article","venue":"Alzheimer s Research & Therapy","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; Engineering and Physical Sciences Research Council; National Institute for Health and Care Research; Canadian Institutes of Health Research; Alzheimer Society; Medical Research Council; Alzheimer's Society","keywords":"Voxel; Artificial intelligence; Neuroimaging; Classifier (UML); Positron emission tomography; Pattern recognition (psychology); Medicine; Support vector machine; Pet imaging; Alzheimer's disease; Machine learning; Computer science; Nuclear medicine; Pathology; 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":[0.00327188,0.0008583259,0.001452326,0.0009916116,0.0004611568,0.001368514,0.001710263,0.002279069,0.0009111558],"category_scores_gemma":[0.01006248,0.0005443115,0.001104444,0.0007979464,0.0006307687,0.0009793984,0.001016476,0.001987359,0.0005396735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001254256,"about_ca_system_score_gemma":0.001565588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004599724,"about_ca_topic_score_gemma":0.003056872,"domain_scores_codex":[0.9983607,0.0005725494,0.0001328679,0.0004233216,0.000375035,0.0001356278],"domain_scores_gemma":[0.9926844,0.004648178,0.0004416323,0.0002884126,0.00176343,0.0001739307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00049085,0.0003225403,0.00486325,0.00009876667,0.0001101431,0.0002130134,0.0001103513,0.7513201,0.009614653,0.001792034,0.001411807,0.2296525],"study_design_scores_gemma":[0.000006342137,0.00002789619,0.0001556287,0.000003378949,0.000004311774,0.00001287776,0.000003298039,0.9981919,0.0009507555,0.0005749458,0.0000645307,0.000004214456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09537205,0.0002893197,0.9018328,0.0005321185,0.00006560252,0.0001108355,0.0001174869,0.001062613,0.0006171839],"genre_scores_gemma":[0.7719948,0.00008497434,0.2261456,0.0002904021,0.00008627168,0.0001961888,0.0002625259,0.0000549737,0.0008843323],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004599724,"threshold_uncertainty_score":0.01730353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2392108352439184,"score_gpt":0.4682803148708781,"score_spread":0.2290694796269596,"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."}}