{"id":"W4386537575","doi":"10.1093/neuonc/noad137.004","title":"PL02.2.A RAPID, COMPREHENSIVE REPORTING OF MOLECULAR DIAGNOSTICS WITH RAPID-CNS2: A PROSPECTIVE VALIDATION COHORT","year":2023,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Glioma Diagnosis and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research","keywords":"Indel; Turnaround time; Concordance; DNA sequencing; Nanopore sequencing; Computational biology; Subtyping; Molecular diagnostics; Copy-number variation; Medical diagnosis; Whole genome sequencing; Biology; Medicine; Bioinformatics; Genetics; Computer science; Genome; Gene; Single-nucleotide polymorphism; Pathology; Genotype","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.004521692,0.0009554546,0.0004398689,0.0007195107,0.0008831165,0.001219255,0.001063551,0.0008117209,0.004847373],"category_scores_gemma":[0.00593831,0.0006034707,0.000574719,0.0005376414,0.0006969071,0.0005656971,0.001472376,0.0007847931,0.003564258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006214613,"about_ca_system_score_gemma":0.001133038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002978497,"about_ca_topic_score_gemma":0.001613649,"domain_scores_codex":[0.9981754,0.0005016429,0.000143051,0.0006843625,0.0003227503,0.0001729399],"domain_scores_gemma":[0.9965252,0.0005508803,0.0003612127,0.001259826,0.000846297,0.0004565086],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01057457,0.002626109,0.8658563,0.000437543,0.0005272154,0.002727748,0.001322872,0.001691076,0.04655828,0.00123739,0.02234303,0.04409792],"study_design_scores_gemma":[0.002597395,0.01538783,0.8275437,0.0002460091,0.0008477715,0.01624897,0.001049865,0.01044387,0.04456532,0.001681859,0.0791868,0.0002006323],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9554342,0.0003651462,0.01380885,0.0002784756,0.00006901673,0.001898277,0.02439553,0.0005098631,0.003240748],"genre_scores_gemma":[0.9049156,0.0001973165,0.01384793,0.0007464036,0.00009129439,0.002844254,0.07272996,0.0006642045,0.003963128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004847373,"threshold_uncertainty_score":0.02391332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03095604419962527,"score_gpt":0.317228147366008,"score_spread":0.2862721031663827,"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."}}