{"id":"W3181077847","doi":"10.1101/2021.07.08.451713","title":"caliPER: A software for blood-free parametri <i>c</i> P <i>a</i> t <i>l</i> ak mapp <i>i</i> ng using <i>PE</i> T/M <i>R</i> I input function","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital; University of Saskatchewan; Lawson Health Research Institute; Siemens (Canada); Western University","funders":"Canadian Institutes of Health Research","keywords":"Calipers; Blood sampling; Nuclear medicine; Statistical parametric mapping; Arterial blood; Positron emission tomography; Biomedical engineering; Sampling (signal processing); Medicine; Mathematics; Computer science; Radiology; Magnetic resonance imaging; Internal medicine; Computer vision","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":[0.001815956,0.001968533,0.001219114,0.001741181,0.0005083139,0.002073515,0.003338043,0.001156031,0.06958245],"category_scores_gemma":[0.005796775,0.00137076,0.001550592,0.0008946012,0.0006793853,0.001712869,0.002235527,0.00265044,0.01780601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001050459,"about_ca_system_score_gemma":0.001943168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00442998,"about_ca_topic_score_gemma":0.004128461,"domain_scores_codex":[0.9989864,0.0000968524,0.0001492516,0.0002836299,0.0004113364,0.00007254781],"domain_scores_gemma":[0.9974188,0.001343329,0.0002604917,0.00032944,0.0005209782,0.0001269625],"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":[0.001502607,0.0002531353,0.002984972,0.002199155,0.0005153613,0.0009024358,0.000852098,0.0251057,0.05174863,0.01261931,0.3578893,0.5434273],"study_design_scores_gemma":[0.000659992,0.0002147314,0.005818272,0.0003807967,0.0002028498,0.0009503082,0.0001530944,0.400654,0.1281823,0.01525887,0.4470304,0.0004943901],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003581418,0.0003009402,0.5117348,0.0001305632,0.0001281192,0.0003765132,0.007255195,0.4726849,0.003807507],"genre_scores_gemma":[0.07998316,0.0008273657,0.6969997,0.0007315943,0.0001213544,0.003195357,0.02450384,0.170499,0.02313867],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06958245,"threshold_uncertainty_score":0.2327765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02357143205495551,"score_gpt":0.257775017179091,"score_spread":0.2342035851241355,"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."}}