{"id":"W2099744219","doi":"10.1161/strokeaha.110.588335","title":"Gene Expression Profiling of Blood for the Prediction of Ischemic Stroke","year":2010,"lang":"en","type":"article","venue":"Stroke","topic":"Cerebrovascular and genetic disorders","field":"Medicine","cited_by":149,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Research Resources; National Institute of Neurological Disorders and Stroke; U.S. Public Health Service; University of Cincinnati; Canadian Institutes of Health Research; MIND Institute, University of California, Davis","keywords":"Medicine; Ischemic stroke; Stroke (engine); Gene expression profiling; Profiling (computer programming); Internal medicine; Gene; Gene expression; Cardiology; Ischemia; Genetics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001419941,0.00006761884,0.0001463982,0.00003567587,0.00003166127,0.000002234982,0.00007299405,0.00006840206,0.00007221593],"category_scores_gemma":[0.00009759534,0.00004502869,0.0001563612,0.00003905086,0.00006750985,0.00001653517,0.00002235292,0.0001161826,7.483004e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001789165,"about_ca_system_score_gemma":0.00004938106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001379456,"about_ca_topic_score_gemma":0.00000605167,"domain_scores_codex":[0.9993944,0.000006148878,0.0001900862,0.000119998,0.0001807045,0.0001086974],"domain_scores_gemma":[0.9994324,0.00005039167,0.00007865915,0.000307077,0.0000981288,0.00003329517],"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":[0.00007727209,0.00008732994,0.01867137,0.0001006759,0.0001115362,8.718001e-8,0.0000883986,0.000009070524,0.976642,0.00001620939,0.0003607536,0.003835344],"study_design_scores_gemma":[0.00181044,0.0001935212,0.007089329,0.00002698756,0.0003415203,0.000006184647,0.0002439539,0.0002526252,0.9880054,0.000009512824,0.001988922,0.00003161265],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934481,0.0003984754,0.002890041,0.00007413297,0.0002229083,0.0005443098,0.0001454597,0.00001390484,0.002262606],"genre_scores_gemma":[0.9899327,0.00004957751,0.009052992,0.00001314476,0.0001277351,0.00003537085,0.00003263328,0.00001176176,0.0007440527],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01158205,"threshold_uncertainty_score":0.1836217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01013231810759898,"score_gpt":0.2442789527821874,"score_spread":0.2341466346745884,"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."}}