{"id":"W2180061851","doi":"10.1126/sciadv.1500417","title":"Fractal circuit sensors enable rapid quantification of biomarkers for donor lung assessment for transplantation","year":2015,"lang":"en","type":"article","venue":"Science Advances","topic":"Transplantation: Methods and Outcomes","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Lung transplantation; Biomarker; Transplantation; Medicine; Lung; Profiling (computer programming); Clinical Practice; Gene expression profiling; Molecular biomarkers; Computational biology; Bioinformatics; Pathology; Gene expression; Computer science; Oncology; Internal medicine; Biology; Gene","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001353805,0.00009090976,0.0001973073,0.0001666104,0.0001305427,0.00002218028,0.0001077547,0.000032213,0.000006830169],"category_scores_gemma":[0.0001612093,0.0000733633,0.00007260589,0.0003474996,0.0002288652,0.0006026521,0.000002436863,0.00003403197,6.732074e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006247783,"about_ca_system_score_gemma":0.0003505557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001352113,"about_ca_topic_score_gemma":0.000007358259,"domain_scores_codex":[0.9988555,0.00002331195,0.0002781464,0.0002808863,0.0003457212,0.0002164829],"domain_scores_gemma":[0.998947,0.0003054808,0.0001531969,0.0001431826,0.000341939,0.0001092161],"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":[0.001360002,0.0001966201,0.05220886,0.001986128,0.00007231941,0.000003389242,0.00258527,0.0009650901,0.787244,0.005858363,0.0001139138,0.1474061],"study_design_scores_gemma":[0.00588078,0.001035431,0.08762699,0.0002535101,0.0003976237,0.00004045499,0.003952875,0.009631712,0.8810986,0.004716184,0.005029093,0.0003367706],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3166891,0.0004859978,0.6794787,0.0005365728,0.0006311382,0.001186954,0.0001068617,0.00003713173,0.0008475393],"genre_scores_gemma":[0.773569,0.0001137252,0.2259635,0.00004910048,0.00005140805,0.00008638649,0.00006985564,0.000007768972,0.00008925775],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4568799,"threshold_uncertainty_score":0.2991669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0678532051074791,"score_gpt":0.4052447927859087,"score_spread":0.3373915876784296,"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."}}