{"id":"W3154723095","doi":"10.3390/biom11050621","title":"Overcoming the Challenges of High Quality RNA Extraction from Core Needle Biopsy","year":2021,"lang":"en","type":"article","venue":"Biomolecules","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; London Health Sciences Centre","funders":"Amsterdam University Medical Centers; Agentschap Innoveren en Ondernemen","keywords":"Core (optical fiber); Extraction (chemistry); Core biopsy; Biopsy; RNA; RNA extraction; Computer science; Computational biology; Medicine; Radiology; Biology; Internal medicine; Chemistry; Chromatography; Biochemistry; Gene; Telecommunications","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":[0.01225052,0.000931857,0.001243175,0.001855896,0.0009585215,0.002449295,0.001193086,0.001350638,0.003090638],"category_scores_gemma":[0.0137876,0.0008072063,0.0006648993,0.001110535,0.001665712,0.001375463,0.001820107,0.002402601,0.004240457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005463402,"about_ca_system_score_gemma":0.001462369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005935135,"about_ca_topic_score_gemma":0.001683001,"domain_scores_codex":[0.9903235,0.003615525,0.000899455,0.00163835,0.003200096,0.0003229471],"domain_scores_gemma":[0.9822825,0.009157948,0.001457646,0.002846815,0.003992765,0.0002623825],"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":[0.0002449802,0.00007207689,0.003164791,0.002465867,0.0001206378,0.0005853949,0.00055385,0.0009352259,0.8832775,0.002221015,0.003735729,0.1026229],"study_design_scores_gemma":[0.00005765608,0.0008492488,0.02160526,0.001287226,0.0003357209,0.005191792,0.0009296678,0.01050306,0.7732209,0.01093913,0.1749374,0.0001429156],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07265465,0.0225998,0.8898319,0.003359787,0.001331792,0.001329491,0.001329212,0.001922437,0.005640916],"genre_scores_gemma":[0.1784539,0.02030064,0.7803959,0.003333649,0.001179837,0.00248608,0.005003605,0.00203961,0.00680671],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01225052,"threshold_uncertainty_score":0.06478769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03650955022371093,"score_gpt":0.3032695645553403,"score_spread":0.2667600143316293,"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."}}