{"id":"W4309343191","doi":"10.1101/2022.11.17.22282418","title":"CT perfusion stroke lesion threshold calibration between deconvolution algorithms","year":2022,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lawson Health Research Institute; Western University","funders":"","keywords":"Penumbra; Deconvolution; Imaging phantom; Nuclear medicine; Cerebral blood flow; Algorithm; Medicine; Stroke (engine); Perfusion; Ground truth; Perfusion scanning; Mathematics; Radiology; Computer science; Physics; Internal medicine; Artificial intelligence; Ischemia","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009240649,0.0007432968,0.0006379246,0.0009222241,0.0004116659,0.001418509,0.001390363,0.0008165272,0.002584205],"category_scores_gemma":[0.05369975,0.0005850936,0.0009199402,0.0005682974,0.0005743369,0.0006461935,0.001321497,0.0008737867,0.0009933851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00138033,"about_ca_system_score_gemma":0.001057892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001467371,"about_ca_topic_score_gemma":0.001206379,"domain_scores_codex":[0.9940922,0.002227728,0.0007629651,0.001361196,0.001380107,0.0001758584],"domain_scores_gemma":[0.977703,0.010009,0.002610115,0.003544391,0.005925839,0.0002075818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.01103335,0.0006357812,0.1639883,0.001196905,0.0009324217,0.0004453605,0.001494658,0.07871874,0.1638426,0.002677456,0.004778801,0.5702556],"study_design_scores_gemma":[0.0007035386,0.003866661,0.2347151,0.0003349031,0.0008720812,0.002985053,0.0004357542,0.3585159,0.382418,0.006450851,0.00826762,0.0004346063],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5019129,0.0009584365,0.4880591,0.0002402225,0.0002895902,0.001014416,0.0009066859,0.002089142,0.004529542],"genre_scores_gemma":[0.8662884,0.0001423974,0.1311996,0.0001547838,0.00004479593,0.0005431148,0.0006281868,0.0003731157,0.0006255262],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009240649,"threshold_uncertainty_score":0.04886979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04537832437474016,"score_gpt":0.3044105887786289,"score_spread":0.2590322644038887,"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."}}