{"id":"W4396890326","doi":"10.1038/s41597-024-03295-z","title":"ReMIND: The Brain Resection Multimodal Imaging Database","year":2024,"lang":"en","type":"article","venue":"Scientific Data","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"National Institutes of Health; Brigham and Women's Hospital; National Institute of Biomedical Imaging and Bioengineering; U.S. Department of Health and Human Services","keywords":"Neuronavigation; Magnetic resonance imaging; Interventional magnetic resonance imaging; Medicine; Intraoperative MRI; Workflow; Database; Radiology; Brain tumor; Computer science; Pathology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004000547,0.00009594407,0.0001020478,0.0001588052,0.0003675639,0.0006483699,0.0006583532,0.00002136183,0.0002690498],"category_scores_gemma":[0.00192438,0.00005980715,0.00003834254,0.000530416,0.0004008165,0.0004035265,0.0005990312,0.0005618252,0.000259868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003964611,"about_ca_system_score_gemma":0.0001800691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001343323,"about_ca_topic_score_gemma":0.00001940102,"domain_scores_codex":[0.9982493,0.00006500511,0.0001862788,0.0007473551,0.0004845863,0.0002674808],"domain_scores_gemma":[0.997581,0.0002297834,0.00002652729,0.001997258,0.0000396447,0.0001257817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001426349,0.00002308432,0.0003606309,0.00008018922,0.00001968249,0.0001387255,0.0002076313,0.000004535399,0.02381172,0.0003046001,0.8391756,0.1358593],"study_design_scores_gemma":[0.0001601461,0.000006362093,0.0004178158,0.0001962266,0.00003692649,0.0001827434,0.0001054131,0.4003399,0.0002821119,0.0001117053,0.5981057,0.0000549348],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1815903,0.01869316,0.1125076,0.5893998,0.05235756,0.002573154,0.005519148,0.00200914,0.03535012],"genre_scores_gemma":[0.927093,0.00005723917,0.009525623,0.003553299,0.002297868,0.00001818114,0.0155651,0.00008344003,0.04180623],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7455027,"threshold_uncertainty_score":0.6252246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03263658663287678,"score_gpt":0.3593157824915389,"score_spread":0.3266791958586622,"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."}}