{"id":"W7117746507","doi":"10.64898/2025.12.30.696985","title":"A whole-organ multi-scale in silico framework for human kidney haemodynamics informed by hierarchical phase-contrast tomography","year":2025,"lang":"","type":"article","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced X-ray Imaging Techniques","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University College London; National Institute for Health and Care Research; Great Ormond Street Hospital for Children; European Synchrotron Radiation Facility; Royal Academy of Engineering; Wellcome Trust; Medical Research Council; Canadian Institute for Advanced Research; Silicon Valley Community Foundation","keywords":"Kidney; Hemodynamics; Renal function; Glomerulus; Renal blood flow; Renal circulation; Blood flow; Perfusion; Stenosis","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005696559,0.001150886,0.00126912,0.000903265,0.0006685607,0.000488176,0.001190644,0.0006673928,0.00003604588],"category_scores_gemma":[0.0003979135,0.001408789,0.0004955742,0.002425503,0.000685518,0.0007342019,0.0003291991,0.001739399,0.00001424111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005035611,"about_ca_system_score_gemma":0.001180005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000730756,"about_ca_topic_score_gemma":0.00001011362,"domain_scores_codex":[0.9946182,0.0001611004,0.001516216,0.001564374,0.0003817842,0.001758372],"domain_scores_gemma":[0.9961451,0.0003810116,0.0005858173,0.001648975,0.0004915262,0.0007475926],"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":[0.0002404997,0.003369344,0.06997709,0.0005047679,0.0004139153,0.000007234684,0.00007361639,0.00001611254,0.8884038,0.03517029,0.001684963,0.0001383739],"study_design_scores_gemma":[0.02140837,0.0008344378,0.03299597,0.005705425,0.0006535089,1.626402e-8,0.00009457934,0.02191462,0.8261192,0.002458891,0.08326133,0.004553623],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.313183,0.000356785,0.6792338,0.0009217793,0.0004639495,0.002596024,0.002732376,0.0004991993,0.00001304428],"genre_scores_gemma":[0.8455591,0.00003342204,0.1520815,0.0008453406,0.0002826244,0.0009653242,0.000009942817,0.0002043706,0.00001831962],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5323762,"threshold_uncertainty_score":0.9988362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008752682837833847,"score_gpt":0.282579335817158,"score_spread":0.2738266529793241,"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."}}