{"id":"W2901971624","doi":"10.1002/hbm.24463","title":"Quantitative assessment of field strength, total intracranial volume, sex, and age effects on the goodness of harmonization for volumetric analysis on the ADNI database","year":2018,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Mental Health; National Institute on Aging; Canadian Institutes of Health Research; National Institute of Nursing Research; Alzheimer Society Research Program; Natural Sciences and Engineering Research Council of Canada; Alzheimer's Disease Neuroimaging Initiative; Canadian Bee Research Fund; National Institutes of Health; Michael Smith Health Research BC","keywords":"Covariate; Database; Harmonization; Computer science; Goodness of fit; Confounding; Statistics; Data mining; Mathematics; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003739173,0.00009458447,0.0002157192,0.000194505,0.000214744,0.00001360509,0.00009358393,0.00002746259,0.00002316906],"category_scores_gemma":[0.0006344158,0.00006243806,0.00006621414,0.0006154447,0.0001156485,0.00003370262,0.00004306803,0.0001269118,3.641771e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001721492,"about_ca_system_score_gemma":0.00001330943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002150832,"about_ca_topic_score_gemma":0.000006932326,"domain_scores_codex":[0.9992594,0.00007713532,0.0002055741,0.0002068993,0.0001494071,0.0001016468],"domain_scores_gemma":[0.9976107,0.00174323,0.0001772457,0.0003504112,0.0000941032,0.00002427677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002934471,0.0009246464,0.03793035,0.0008384956,0.00106198,0.00001344259,0.002646977,0.00009571953,0.5933043,0.323061,0.01408027,0.02574938],"study_design_scores_gemma":[0.002099574,0.005574491,0.7579185,0.0008034238,0.001151688,0.00000618471,0.001593824,0.1729884,0.05016318,0.005487205,0.001794107,0.0004194619],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6879127,0.00000811013,0.3089558,0.002097367,0.00001479425,0.0006954778,0.00003071493,0.00002398032,0.0002610966],"genre_scores_gemma":[0.9882581,0.00000487187,0.01076,0.0006379558,0.00006758621,0.00008169958,0.00004758015,0.00001118223,0.0001310658],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7199881,"threshold_uncertainty_score":0.254615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08452248102561821,"score_gpt":0.3890073806962967,"score_spread":0.3044848996706785,"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."}}