{"id":"W4389965997","doi":"10.1111/his.15110","title":"Lessons from genomic profiling: towards a molecular‐based classification of ovarian Sertoli–Leydig cell tumour","year":2023,"lang":"en","type":"letter","venue":"Histopathology","topic":"Ovarian cancer diagnosis and treatment","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; BC Cancer Agency","funders":"Deutsche Forschungsgemeinschaft","keywords":"Sertoli cell; Profiling (computer programming); Leydig cell; Biology; Computational biology; Gene expression profiling; Pathology; Oncology; Internal medicine; Medicine; Bioinformatics; Hormone; Computer science; Gene; Genetics; Spermatogenesis; Gene expression; Luteinizing hormone","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000163834,0.0004545457,0.001105293,0.0004058026,0.00005861564,0.00001315278,0.0002569028,0.00102153,0.0001921562],"category_scores_gemma":[0.00005243874,0.0004423719,0.000378969,0.0002285847,0.0001659146,0.00001938323,0.00006638385,0.0009927403,0.0002759783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007347505,"about_ca_system_score_gemma":0.001157198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005531658,"about_ca_topic_score_gemma":0.00002488659,"domain_scores_codex":[0.9973274,0.0002147401,0.0006536533,0.0009936837,0.0003494394,0.0004610298],"domain_scores_gemma":[0.9980263,0.0001135473,0.0005432411,0.001046524,0.0001667466,0.0001036035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0003465396,0.0009883374,0.00739013,0.001590985,0.0001707398,0.02597094,0.0004828448,0.00002672602,0.3207066,0.0005433435,0.638922,0.002860864],"study_design_scores_gemma":[0.007641499,0.001300474,0.4759479,0.0006743459,0.00383987,0.0002331598,0.00008224225,0.000514614,0.04275723,0.0008269649,0.4649894,0.001192234],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.07460064,0.002074643,0.001113779,0.9089652,0.002283429,0.001738474,0.001563754,0.0002999202,0.007360199],"genre_scores_gemma":[0.6720871,0.0002627292,0.01177607,0.2921149,0.003293207,0.001378342,0.01361368,0.0005376178,0.004936378],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.6168503,"threshold_uncertainty_score":0.9998028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05427949231309776,"score_gpt":0.3019766905206135,"score_spread":0.2476971982075157,"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."}}