{"id":"W4293662930","doi":"10.1002/path.6006","title":"The proteome of clear cell ovarian carcinoma","year":2022,"lang":"en","type":"article","venue":"The Journal of Pathology","topic":"Ovarian cancer diagnosis and treatment","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Canada's Michael Smith Genome Sciences Centre; Calgary Laboratory Services; BC Cancer Agency; University of British Columbia","funders":"Canadian Cancer Society Research Institute; Canadian Institutes of Health Research; National Cancer Institute; BC Cancer Foundation; Ovarian Cancer Canada; Terry Fox Research Institute; Canada Research Chairs; VGH and UBC Hospital Foundation","keywords":"Proteome; Ovarian carcinoma; Carcinoma; Computational biology; Biology; Pathology; Cancer research; Medicine; Oncology; Ovarian cancer; Bioinformatics; Internal medicine; Cancer","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":[0.001026879,0.00006132531,0.0002084576,0.0000454087,0.0001789467,0.000003045355,0.000176195,0.00002064231,0.0002118077],"category_scores_gemma":[0.00002768404,0.00002940392,0.0001030729,0.0000982408,0.00009394723,0.00001367941,0.00007436873,0.0002998101,0.000004397551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007468073,"about_ca_system_score_gemma":0.0001517482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002462506,"about_ca_topic_score_gemma":0.000003059809,"domain_scores_codex":[0.999,0.0003085339,0.0002970365,0.00005254159,0.0002123491,0.0001296013],"domain_scores_gemma":[0.9991349,0.0001577388,0.0003500216,0.0002392541,0.00007690615,0.00004119963],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.03696108,0.01034387,0.3968934,0.0005200233,0.002541139,0.02905406,0.05098076,0.001926981,0.2922668,0.01701947,0.08632588,0.07516658],"study_design_scores_gemma":[0.01380772,0.0283317,0.7691969,0.00006419635,0.001812794,0.03217442,0.007415137,0.00007791261,0.0435673,0.003946957,0.09937265,0.0002322871],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9838388,0.002138984,0.00002924654,0.01191227,0.0003398827,0.0002342695,0.000006998839,0.000002424983,0.001497131],"genre_scores_gemma":[0.9987861,0.000204279,0.0001943739,0.0003362679,0.0001397725,0.00001158872,4.862133e-7,0.000009692176,0.0003174421],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3723035,"threshold_uncertainty_score":0.2319146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0137837706961416,"score_gpt":0.2487427080888197,"score_spread":0.2349589373926781,"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."}}