{"id":"W4394362717","doi":"10.6084/m9.figshare.21220142","title":"Additional file 4 of Liquid biopsy using ascitic fluid and pleural effusion supernatants for genomic profiling in gastrointestinal and lung cancers","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Pleural effusion; Pleural fluid; Pathology; Profiling (computer programming); Medicine; Lung; Liquid biopsy; Ascitic fluid; Effusion; Internal medicine; Computer science; Cancer; Surgery; Ascites","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001351844,0.001475033,0.001742996,0.0019841,0.0007312768,0.002117777,0.002174922,0.002134065,0.5584556],"category_scores_gemma":[0.01504724,0.0006243798,0.001417903,0.002976078,0.0003746279,0.00126792,0.001246235,0.001337802,0.1064265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001323396,"about_ca_system_score_gemma":0.001819507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008420619,"about_ca_topic_score_gemma":0.016374,"domain_scores_codex":[0.9991243,0.0001286719,0.0001328962,0.0003238317,0.0001533871,0.0001368399],"domain_scores_gemma":[0.9915162,0.005824136,0.0005972033,0.0007884809,0.0009332504,0.0003405898],"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":[0.0004560995,0.00007480417,0.003857602,0.003478412,0.0001337484,0.00007595999,0.00003333881,0.0005969931,0.0002312196,0.0004970364,0.9852799,0.005284884],"study_design_scores_gemma":[0.005595091,0.0002359542,0.03641282,0.003338167,0.000452041,0.0006512564,0.0001831602,0.001984838,0.00117879,0.008530547,0.9413078,0.0001295393],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00008355651,0.00002298354,0.00005070617,0.00002945999,0.00000782274,0.00001284792,0.9995372,0.00007872573,0.000176544],"genre_scores_gemma":[0.001828156,0.0000654325,0.0004624554,0.0001588705,0.00002479512,0.0002847684,0.9958122,0.0001357873,0.0012274],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5584556,"threshold_uncertainty_score":0.6298095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01847808427396978,"score_gpt":0.2582037837283403,"score_spread":0.2397256994543705,"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."}}