{"id":"W4405065238","doi":"10.1101/2024.12.03.24317221","title":"Assessing the diagnostic impact of blood transcriptome profiling in a pediatric cohort previously assessed by genome sequencing","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; SickKids Foundation; Hospital for Sick Children","funders":"","keywords":"Profiling (computer programming); Genome; Cohort; Computational biology; Transcriptome; Whole genome sequencing; Genomics; Biology; Genetics; Medicine; Gene; Computer science; Internal medicine; Gene expression","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":[0.001436596,0.0003962326,0.0003061722,0.0006651937,0.0004877551,0.0009326671,0.0003408389,0.000433598,0.0009611442],"category_scores_gemma":[0.00439495,0.0002163492,0.0002382385,0.0004369592,0.0003932215,0.0002925818,0.000548833,0.0007014995,0.0002835977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002387117,"about_ca_system_score_gemma":0.0002959698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001977932,"about_ca_topic_score_gemma":0.001924711,"domain_scores_codex":[0.9988726,0.0003349055,0.00005319531,0.0003983712,0.0002198577,0.0001211354],"domain_scores_gemma":[0.997747,0.0009822965,0.0005328601,0.0002260596,0.0002839997,0.0002276791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002296066,0.000036093,0.9805074,0.00002093953,0.00005746344,0.0006261063,0.0002418574,0.0001926346,0.0122423,0.00009656189,0.0004324613,0.005316633],"study_design_scores_gemma":[0.00001622797,0.0003833355,0.985748,0.00002717417,0.0001179204,0.003125474,0.0005342129,0.002165271,0.005766433,0.0002390273,0.001863473,0.00001354275],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969977,0.0002463306,0.001502627,0.00008547361,0.00001282725,0.00001430197,0.000634438,0.00002526277,0.0004809691],"genre_scores_gemma":[0.9965588,0.0001687925,0.002069709,0.0001422916,0.00002541046,0.00002565019,0.0008225546,0.00002831111,0.0001583198],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001977932,"threshold_uncertainty_score":0.007597506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01387234852902422,"score_gpt":0.2940575391732118,"score_spread":0.2801851906441876,"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."}}