{"id":"W4309619646","doi":"10.1117/1.jbo.27.11.116005","title":"Assessing jugular venous compliance with optical hemodynamic imaging by modulating intrathoracic pressure","year":2022,"lang":"en","type":"article","venue":"Journal of Biomedical Optics","topic":"Cerebral Venous Sinus Thrombosis","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Toronto Rehabilitation Institute; University Health Network; Research Institute for Aging","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Compliance (psychology); Hemodynamics; Optical imaging; Medicine; Biomedical engineering; Optics; Cardiology","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":[0.0008165756,0.0002266137,0.0007203888,0.0002051731,0.0003259224,0.000104014,0.0002891965,0.0000784846,0.0001724806],"category_scores_gemma":[0.0001910087,0.000184115,0.000151245,0.0005389252,0.0003392518,0.0002754312,0.0001692273,0.00135818,0.000003116018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003505319,"about_ca_system_score_gemma":0.0005238235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007778945,"about_ca_topic_score_gemma":3.233088e-7,"domain_scores_codex":[0.9967906,0.0001059033,0.0007780124,0.0002560581,0.001588383,0.0004810501],"domain_scores_gemma":[0.9983066,0.0001419744,0.0005426035,0.0002625067,0.0002695248,0.0004767932],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001821336,0.01513762,0.0123918,0.001863338,0.002630594,0.01194743,0.00219192,0.00626394,0.6476512,0.0006541733,0.006741025,0.2907056],"study_design_scores_gemma":[0.01190861,0.0151145,0.01636377,0.003010588,0.004417737,0.06843074,0.006427553,0.8506548,0.005693414,0.0006558123,0.01570603,0.001616496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9482409,0.001600439,0.04366429,0.005116375,0.0004274986,0.0002519698,0.00002441284,0.00005119847,0.0006228611],"genre_scores_gemma":[0.9413034,0.00001793399,0.05774117,0.0004717422,0.000314687,0.000003481695,0.0000252443,0.00005655687,0.00006573313],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8443908,"threshold_uncertainty_score":0.7507991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01904311971044256,"score_gpt":0.3044026185311248,"score_spread":0.2853594988206823,"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."}}