{"id":"W4409324328","doi":"10.1016/j.xpro.2025.103705","title":"Protocol for obtaining cancer type and subtype predictions using subSCOPE","year":2025,"lang":"en","type":"article","venue":"STAR Protocols","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre","funders":"National Institutes of Health; Canada's Michael Smith Genome Sciences Centre; National Cancer Institute; Foundation for the National Institutes of Health","keywords":"Protocol (science); Type (biology); Cancer; Computational biology; Computer science; Biology; Medicine; Genetics; Pathology; Paleontology","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":[0.0003463126,0.0001310555,0.0002454318,0.0001135697,0.0002078435,0.00004821231,0.00006719903,0.00007490058,0.00008480843],"category_scores_gemma":[0.0004819743,0.0001085825,0.0000378832,0.0003171353,0.00009128138,0.00006559016,0.00006292319,0.0002588278,0.000001211193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008844983,"about_ca_system_score_gemma":0.0006003146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008730299,"about_ca_topic_score_gemma":0.000005321913,"domain_scores_codex":[0.9990687,0.00003401726,0.0002435212,0.0002659876,0.0001326618,0.0002551358],"domain_scores_gemma":[0.9993373,0.00006715929,0.00007851563,0.0001848973,0.0002246429,0.0001075101],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.01087374,0.001149871,0.5544388,0.0182448,0.001179096,0.00007209028,0.001869002,0.001398185,0.07869525,0.02000386,0.06357557,0.2484997],"study_design_scores_gemma":[0.01257127,0.001074864,0.01350323,0.005391199,0.0001971752,0.00003275881,0.000134965,0.1678779,0.002934794,0.002800419,0.7931275,0.0003539002],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"protocol","genre_gemma":"protocol","genre_scores_codex":[0.003593085,0.00001630973,0.009545558,0.001353321,0.00008426142,0.9825597,0.000007985259,0.0001123939,0.002727378],"genre_scores_gemma":[0.002621342,0.000001070392,0.01518055,0.0006004348,0.0001922645,0.9802234,0.000005298989,0.00002584754,0.00114976],"genre_candidate":"protocol","genre_consensus":"protocol","teacher_disagreement_score":0.729552,"threshold_uncertainty_score":0.4427864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06690811567861747,"score_gpt":0.4626135912829332,"score_spread":0.3957054756043157,"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."}}