{"id":"W4412855610","doi":"10.3389/fnume.2025.1632112","title":"On the construction of a large-scale database of AI-assisted annotating lung ventilation-perfusion scintigraphy for pulmonary embolism (VQ4PEDB)","year":2025,"lang":"en","type":"article","venue":"Frontiers in Nuclear Medicine","topic":"Venous Thromboembolism Diagnosis and Management","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Centre de Santé et de Services Sociaux de la Montagne; Ottawa Hospital; St. Michael's Hospital; Hôpital Maisonneuve-Rosemont; Merck Canada Inc. (Canada); Jewish General Hospital; University of Ottawa; Carleton University","funders":"Mitacs; Government of Canada","keywords":"Pulmonary embolism; Artificial intelligence; Medicine; Computer science; Quality assurance; Medical physics; Database; Radiology; Nuclear medicine; Machine learning; Surgery; Pathology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.003444203,0.001240857,0.0008725787,0.00557892,0.0009721974,0.00232648,0.002871628,0.001632435,0.004246756],"category_scores_gemma":[0.01152807,0.000667881,0.001596397,0.003834614,0.0007394056,0.001573966,0.003685741,0.001191938,0.004319827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002239083,"about_ca_system_score_gemma":0.0031516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03117201,"about_ca_topic_score_gemma":0.04361613,"domain_scores_codex":[0.9973583,0.0004841215,0.0003292448,0.001035055,0.0006419283,0.0001514392],"domain_scores_gemma":[0.9939185,0.001863601,0.0004585154,0.001636008,0.001583148,0.0005401581],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002288972,0.001801236,0.1018674,0.005416263,0.0008421413,0.006197743,0.002802663,0.06530895,0.04971237,0.01008416,0.3425988,0.4110794],"study_design_scores_gemma":[0.0005414149,0.0008912756,0.1193688,0.001196404,0.0004797123,0.003265469,0.003292497,0.4297591,0.04274599,0.01092676,0.3871076,0.000425113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2089997,0.002209372,0.1577758,0.002067152,0.0005348788,0.003291745,0.5567631,0.05825454,0.01010377],"genre_scores_gemma":[0.1187848,0.0005007756,0.1643807,0.0004119975,0.00003920318,0.001127125,0.7118317,0.000774889,0.002148776],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03117201,"threshold_uncertainty_score":0.06198114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008189811370980054,"score_gpt":0.2768651775105858,"score_spread":0.2686753661396057,"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."}}