{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003574403,0.0005459475,0.001006767,0.0009784097,0.001076745,0.00193173,0.001198418,0.01090411,0.001396547],"category_scores_gemma":[0.01753935,0.0004943094,0.0007059291,0.0005279115,0.00256172,0.004243553,0.0008950726,0.01573998,0.002215058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002294224,"about_ca_system_score_gemma":0.001055035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001352602,"about_ca_topic_score_gemma":0.002014895,"domain_scores_codex":[0.9981345,0.0007706418,0.0003191712,0.0002165967,0.0004181124,0.0001410129],"domain_scores_gemma":[0.9925448,0.004191831,0.0003178746,0.0004393024,0.001864309,0.0006418157],"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":[0.0002812873,0.0001206566,0.0130914,0.0004082919,0.00009069731,0.02994875,0.000546978,0.001005352,0.004562599,0.01670417,0.742131,0.1911088],"study_design_scores_gemma":[0.0003309612,0.0003562172,0.01099954,0.001078236,0.0001784152,0.1384698,0.001571983,0.008598618,0.002289393,0.168983,0.6669295,0.0002142141],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.003171013,0.007164333,0.001705662,0.9759773,0.009691567,0.00001846686,0.00005961319,0.00004765264,0.002164358],"genre_scores_gemma":[0.08479922,0.02477102,0.007702641,0.6309007,0.2466784,0.00009476315,0.0001977812,0.0000841295,0.004771321],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.01090411,"threshold_uncertainty_score":0.01890349,"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."}}