{"id":"W2937525175","doi":"10.1101/608323","title":"Biomarker Localization, Analysis, Visualization, Extraction, and Registration (BLAzER) Workflow for Research and Clinical Brain PET Applications","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Eli Lilly and Company; U.S. Department of Defense; University of Alabama; Northern California Institute for Research and Education; University of Alabama at Birmingham; Pfizer; BioClinica; Biogen; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; F. Hoffmann-La Roche; Alzheimer's Drug Discovery Foundation; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Segmentation; Workflow; Neuroimaging; Imaging biomarker; Standardized uptake value; Visualization; Biomarker; Artificial intelligence; Pattern recognition (psychology); Computer science; Nuclear medicine; Medicine; Magnetic resonance imaging; Positron emission tomography; Psychology; Neuroscience; Radiology; Chemistry; Database","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":[0.01554904,0.001957922,0.001031693,0.002387409,0.001086607,0.003560923,0.002367591,0.001704234,0.00602942],"category_scores_gemma":[0.02437872,0.001546768,0.001651465,0.001225726,0.0009673481,0.002458889,0.002460402,0.00182727,0.004727642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009963665,"about_ca_system_score_gemma":0.003520978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002904862,"about_ca_topic_score_gemma":0.005094682,"domain_scores_codex":[0.9946563,0.001952205,0.0006688242,0.001244403,0.00127864,0.0001995392],"domain_scores_gemma":[0.9835069,0.005837374,0.002100906,0.003850693,0.004334172,0.000370018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003529861,0.0004591241,0.01790572,0.00149091,0.00104243,0.0006085164,0.001805967,0.02498825,0.315352,0.01008196,0.03272282,0.5900125],"study_design_scores_gemma":[0.0004993117,0.001229422,0.04012739,0.000321967,0.0004540541,0.002372363,0.0005661354,0.3407684,0.5146622,0.0166042,0.08170376,0.0006907433],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02665257,0.0004180319,0.9232993,0.0004939199,0.0001113574,0.000529509,0.0009413808,0.04624112,0.001312764],"genre_scores_gemma":[0.08805965,0.0002442421,0.9033101,0.0003270973,0.00005001871,0.0009601046,0.001274443,0.004382843,0.001391512],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01554904,"threshold_uncertainty_score":0.08223212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05817256522475225,"score_gpt":0.4057801894100959,"score_spread":0.3476076241853436,"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."}}