{"id":"W2024321694","doi":"10.1159/000106983","title":"3-Tesla versus 1.5-Tesla Magnetic Resonance Diffusion and Perfusion Imaging in Hyperacute Ischemic Stroke","year":2007,"lang":"en","type":"article","venue":"Cerebrovascular Diseases","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Foothills Medical Centre; University of Calgary","funders":"Centre National de la Recherche Scientifique","keywords":"Medicine; Magnetic resonance imaging; Nuclear medicine; Perfusion; Effective diffusion coefficient; Stroke (engine); Diffusion MRI; Perfusion scanning; Ischemic stroke; Penumbra; Radiology; Cardiology; Ischemia; Physics","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002644881,0.0003298706,0.0003998161,0.0002938372,0.0001022412,0.00003874169,0.0001645306,0.00008405036,0.0001711356],"category_scores_gemma":[0.0002550016,0.0003119868,0.0002056401,0.0002948031,0.0001343498,0.0001472495,0.0003378795,0.0002439722,0.00003755791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001821339,"about_ca_system_score_gemma":0.00005476689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007101915,"about_ca_topic_score_gemma":0.000009024835,"domain_scores_codex":[0.9977226,0.00003662605,0.0003876773,0.0006919189,0.0005721577,0.0005889914],"domain_scores_gemma":[0.9987321,0.0001424764,0.00007369048,0.0006525702,0.00007161339,0.0003275106],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001047126,0.0003925339,0.6980835,0.0001933556,0.00005237609,0.000394669,0.0001754307,0.000001469496,0.05423089,0.00002786393,0.01125394,0.2341468],"study_design_scores_gemma":[0.01117528,0.0001818728,0.8814463,0.0003941433,0.0006194227,0.0000823548,0.0007415701,0.0009183241,0.005353152,0.000007211815,0.09862779,0.0004525565],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9530506,0.03798843,0.0001479393,0.0003573986,0.0002054739,0.0005820508,0.00001790214,0.0001195681,0.00753061],"genre_scores_gemma":[0.993923,0.002254452,0.001188163,0.0004829738,0.0002273519,0.00002883939,0.00007151958,0.00006342348,0.001760256],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2336943,"threshold_uncertainty_score":0.9999332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006356345608046134,"score_gpt":0.2355324323969777,"score_spread":0.2291760867889315,"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."}}