{"id":"W6945927218","doi":"10.26226/morressier.5cb58cf5c668520010b56609","title":"INFLUENCE OF RECONSTRUCTION KERNEL AND SLICE THICKNESS ON AUTOMATED ASPECTS PERFORMANCE FOR DETECTION OF EARLY ISCHEMIC CHANGES ON NON-CONTRAST BRAIN COMPUTED TOMOGRAPHY SCANS","year":2017,"lang":"en","type":"other","venue":"BiblioBoard Library Catalog (Open Research Library)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ground truth; Computed tomography; Stroke (engine); Kernel (algebra); Correlation; Occlusion; Siemens; Middle cerebral artery; Image quality","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0116055,0.0007181502,0.0006307326,0.001058501,0.0002449789,0.001280092,0.0005794551,0.0007132388,0.0004795326],"category_scores_gemma":[0.04700541,0.0003964731,0.0006372143,0.0005693107,0.0004464056,0.0006531514,0.0006313677,0.0003023104,0.0002987717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003454422,"about_ca_system_score_gemma":0.0004279255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001361971,"about_ca_topic_score_gemma":0.001400013,"domain_scores_codex":[0.9941007,0.00330953,0.0006783597,0.0008248987,0.0008917181,0.0001946646],"domain_scores_gemma":[0.966494,0.02449366,0.003252246,0.002410123,0.002758527,0.0005914877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.02132035,0.0003482476,0.6875451,0.0004043742,0.001603383,0.0003159776,0.0006972246,0.04988486,0.04299403,0.0002267461,0.0007633693,0.1938963],"study_design_scores_gemma":[0.0002419063,0.003184238,0.7045773,0.0001310646,0.001302765,0.001789364,0.0002905881,0.2291696,0.05798467,0.00040898,0.0007446201,0.0001748715],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9849784,0.0008899908,0.01312469,0.00004105217,0.00001732796,0.00004610683,0.0001441767,0.0003617347,0.0003964317],"genre_scores_gemma":[0.9923362,0.0001087183,0.007171898,0.00001543399,0.000008386897,0.00001869949,0.000199272,0.00008341354,0.00005791491],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0116055,"threshold_uncertainty_score":0.06137651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03427561139785983,"score_gpt":0.3069049686223113,"score_spread":0.2726293572244515,"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."}}