{"id":"W4409869410","doi":"10.1038/s41467-025-58452-8","title":"Investigative needle core biopsies support multimodal deep-data generation in glioblastoma","year":2025,"lang":"en","type":"article","venue":"Nature Communications","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; University of Texas MD Anderson Cancer Center; Break Through Cancer; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology; National Cancer Institute; Ludwig Center at Harvard; Massachusetts Life Sciences Center; National Institutes of Health; Memorial Sloan-Kettering Cancer Center","keywords":"Glioblastoma; Computer science; Core (optical fiber); Medicine; Pathology; Artificial intelligence; Cancer research","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002376448,0.000119142,0.0001252583,0.0001693476,0.0001244349,0.00003203501,0.001403079,0.0003090206,0.000009477643],"category_scores_gemma":[0.0004779918,0.0001251168,0.00004517082,0.0004601723,0.0002420501,0.00001435794,0.001195352,0.0004097321,0.00000485105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003060104,"about_ca_system_score_gemma":0.0001171461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006462156,"about_ca_topic_score_gemma":0.01049175,"domain_scores_codex":[0.9991594,0.0001005066,0.0002274682,0.0003109604,0.0000761835,0.0001254725],"domain_scores_gemma":[0.996677,0.00003060787,0.00007446423,0.003019321,0.0001669394,0.00003159843],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002102492,0.0002278178,0.009302143,0.000009866323,0.0001087016,0.000001691962,0.0001703989,0.00003392439,0.8658509,0.001594112,0.116903,0.005776358],"study_design_scores_gemma":[0.0006345749,0.0001205582,0.009766387,0.00003159199,0.0001069829,0.000006509375,0.0003782524,0.04088248,0.6625188,0.0005661511,0.284564,0.000423725],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9393169,0.0171613,0.003695923,0.009743142,0.0001388306,0.001256554,0.0001634321,0.0001992591,0.02832467],"genre_scores_gemma":[0.9774204,0.0008589789,0.0167392,0.001063073,0.00003993977,0.00005275511,0.003261296,0.00001087584,0.0005534954],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2033321,"threshold_uncertainty_score":0.5854641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03050302377806545,"score_gpt":0.342323806387112,"score_spread":0.3118207826090465,"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."}}