{"id":"W7161983397","doi":"10.82308/50199","title":"Design and optimization of a liquid biopsy collector for ovarian and endometrial cancer screening","year":2020,"lang":"en","type":"dissertation","venue":"","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Endometrial cancer; Liquid biopsy; Medical screening; Epithelial ovarian cancer; Ovarian cancer","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002590722,0.001195473,0.0011622,0.001044951,0.001130313,0.001956172,0.002646501,0.001501348,0.004479732],"category_scores_gemma":[0.002894369,0.000933445,0.0009996078,0.0006505322,0.000595859,0.0009590928,0.001191663,0.000566164,0.001694599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001557243,"about_ca_system_score_gemma":0.003584268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003056764,"about_ca_topic_score_gemma":0.003657908,"domain_scores_codex":[0.9984148,0.0002895711,0.0001028949,0.0003945035,0.0006296738,0.0001685655],"domain_scores_gemma":[0.9979943,0.0004485278,0.000299458,0.000166153,0.0008890615,0.0002025767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00351554,0.0009973418,0.01943831,0.002489425,0.0002820922,0.0007280468,0.0004923787,0.115458,0.6321344,0.004250766,0.004529511,0.2156842],"study_design_scores_gemma":[0.0006426726,0.008178554,0.01773935,0.0001528701,0.000784233,0.000702864,0.0003919085,0.5397288,0.3829615,0.001533771,0.0469111,0.0002722702],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.137193,0.001839684,0.8436223,0.0006024627,0.0005140282,0.003414033,0.0007150414,0.004942278,0.007157237],"genre_scores_gemma":[0.4686791,0.0007337576,0.5189912,0.0003738511,0.00009544942,0.002519687,0.0005944876,0.000325835,0.007686675],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004479732,"threshold_uncertainty_score":0.01498622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01977283603964188,"score_gpt":0.2990084411836933,"score_spread":0.2792356051440514,"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."}}