{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009153821,0.001493747,0.001359685,0.005455814,0.0004268989,0.002032921,0.002238855,0.001537073,0.03801379],"category_scores_gemma":[0.006901099,0.0005219121,0.0006864998,0.006116175,0.0002897023,0.00139961,0.002132786,0.0010689,0.0412924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000732763,"about_ca_system_score_gemma":0.001778377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00512024,"about_ca_topic_score_gemma":0.005889003,"domain_scores_codex":[0.9992455,0.00007677476,0.0002221517,0.0002211333,0.0001563025,0.00007813147],"domain_scores_gemma":[0.9967235,0.0006534847,0.0005596865,0.0009658238,0.0007198534,0.0003778233],"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":[0.0007892993,0.0001074901,0.01008163,0.001887281,0.0001834891,0.0005705271,0.00009989802,0.001180753,0.002366332,0.001261345,0.932905,0.04856706],"study_design_scores_gemma":[0.0006765191,0.0001206937,0.04211346,0.0005568154,0.0002312709,0.001860441,0.0002674237,0.003999138,0.003890844,0.003912501,0.9422221,0.0001488013],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002594283,0.0005632774,0.001220415,0.0001682055,0.00003987427,0.0001024158,0.9909156,0.002712194,0.001683792],"genre_scores_gemma":[0.004966802,0.0002921666,0.001418977,0.000101402,0.00003000627,0.0001870737,0.9923744,0.0001960212,0.0004331648],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03801379,"threshold_uncertainty_score":0.1271688,"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."}}