{"id":"W4409630048","doi":"10.1158/1538-7445.am2025-3896","title":"Abstract 3896: Multi-omic and multi-region profiling of uterine leiomyoma reveals intra- and inter-tumor heterogeneity","year":2025,"lang":"en","type":"article","venue":"Cancer Research","topic":"Uterine Myomas and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; McGill University","funders":"","keywords":"Profiling (computer programming); Uterine leiomyoma; Computational biology; Leiomyoma; Biology; Medicine; Pathology; Computer science","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":[0.0003195915,0.0002435468,0.0003929255,0.0008318214,0.0003312382,0.0004661868,0.0001736181,0.0003060437,0.001122943],"category_scores_gemma":[0.0004736784,0.0001495729,0.0003546114,0.0007372063,0.0001968446,0.0001834334,0.0006203883,0.000322828,0.0003836782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001686688,"about_ca_system_score_gemma":0.0001643117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005374433,"about_ca_topic_score_gemma":0.00139942,"domain_scores_codex":[0.9997113,0.00003232003,0.00002431628,0.0001072597,0.00007501892,0.00004973608],"domain_scores_gemma":[0.9996977,0.00008057669,0.0001003854,0.00004077078,0.00004389682,0.0000368328],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003547641,0.00002421941,0.09357994,0.0002970464,0.0001782191,0.0005882759,0.0001826298,0.0003616806,0.8900591,0.00008303289,0.0005265612,0.01376446],"study_design_scores_gemma":[0.00002251178,0.0002788739,0.8500172,0.00005418678,0.0003617711,0.005133516,0.0005141385,0.004207933,0.1290234,0.0003834533,0.009970566,0.0000325158],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9857197,0.003106503,0.004676255,0.0001650565,0.00002060716,0.00003448,0.005235799,0.0001154452,0.0009261448],"genre_scores_gemma":[0.9864304,0.0005970801,0.006022488,0.0001752732,0.00002155033,0.00005123192,0.005842238,0.00005471776,0.0008050659],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001122943,"threshold_uncertainty_score":0.003756642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.122416861745844,"score_gpt":0.460498550556295,"score_spread":0.338081688810451,"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."}}