{"id":"W4394852361","doi":"10.1101/2024.04.12.588928","title":"Automatization and validation of the hippocampal-to-ventricle ratio in a clinical sample","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; McGill University","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Alzheimer's Association","keywords":"Sample (material); Hippocampal formation; Ventricle; Internal medicine; Computer science; Neuroscience; Medicine; Psychology; Chemistry; Chromatography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006972365,0.0007171042,0.0005424393,0.001649025,0.000693579,0.0008546676,0.001106652,0.001132331,0.00104176],"category_scores_gemma":[0.01434424,0.0003175024,0.0005171174,0.0005092218,0.0009938248,0.0004996405,0.0009817989,0.0005501851,0.0007394359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004104046,"about_ca_system_score_gemma":0.0006850015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003548425,"about_ca_topic_score_gemma":0.005121197,"domain_scores_codex":[0.9966398,0.001073994,0.000312664,0.001454583,0.0003923839,0.0001264346],"domain_scores_gemma":[0.9913992,0.002317845,0.0009440884,0.002013738,0.003016426,0.0003087867],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003458737,0.0009073206,0.7320001,0.0005065925,0.00132975,0.000833847,0.00290337,0.01351105,0.07211838,0.0006878402,0.003313708,0.1684293],"study_design_scores_gemma":[0.000239928,0.001296913,0.8857999,0.000116174,0.0004966037,0.003203027,0.0007191258,0.06593172,0.03739889,0.001122526,0.003583412,0.00009178241],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9791371,0.0004456883,0.01809943,0.00005634713,0.00005010661,0.0003204859,0.0008144074,0.0003427725,0.0007337909],"genre_scores_gemma":[0.9851901,0.00008359763,0.0126563,0.00004677008,0.00002769287,0.0001930437,0.001489003,0.00007940707,0.0002340325],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.006972365,"threshold_uncertainty_score":0.03687382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03570951247529951,"score_gpt":0.2776673005450521,"score_spread":0.2419577880697525,"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."}}