{"id":"W4378363512","doi":"10.1101/2023.05.20.23290209","title":"Robust estimation of dynamic cerebrovascular reactivity using breath-holding fMRI: application in diabetes and hypertension","year":2023,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Baycrest Hospital","funders":"Canadian Institutes of Health Research","keywords":"Functional magnetic resonance imaging; Cardiology; Internal medicine; Diabetes mellitus; Amplitude; Computer science; Medicine; Psychology; Neuroscience; Physics; Endocrinology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000421799,0.0002450092,0.0002737702,0.0002976056,0.00009869832,0.0002254408,0.0002145945,0.000396671,0.0002949847],"category_scores_gemma":[0.001201014,0.000100733,0.0001772974,0.0002176851,0.0001024413,0.0001076427,0.0001702622,0.0002542284,0.00005496716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006288134,"about_ca_system_score_gemma":0.0001255614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001581094,"about_ca_topic_score_gemma":0.00164345,"domain_scores_codex":[0.9999136,0.00003199602,0.000004866113,0.00002657833,0.00001327462,0.00000967924],"domain_scores_gemma":[0.9997281,0.0001554927,0.00003723013,0.00002204336,0.00003699071,0.00002015411],"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":[0.001951997,0.0004666677,0.05609975,0.0003310571,0.0004310615,0.001300055,0.0001896697,0.09019633,0.5432403,0.0005907841,0.0009840586,0.3042182],"study_design_scores_gemma":[0.00007065429,0.0006195474,0.1599203,0.00001745278,0.0001851578,0.0009694101,0.00009526072,0.7830197,0.05329448,0.0009972708,0.0007452052,0.00006556764],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8116505,0.0009277363,0.1861188,0.0001335103,0.00003617398,0.0000484155,0.0002605781,0.0003517786,0.0004724156],"genre_scores_gemma":[0.9632881,0.0002432413,0.03597714,0.00003755265,0.00003293068,0.00002233395,0.0001254165,0.00001601307,0.0002572867],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001581094,"threshold_uncertainty_score":0.003143728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0538484998410241,"score_gpt":0.3120501261236053,"score_spread":0.2582016262825812,"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."}}