{"id":"W2955701310","doi":"10.1088/1361-6560/ab2db4","title":"Evaluating a potential technique with local optical flow vectors for automatic organ-at-risk (OAR) intrusion detection and avoidance during radiotherapy","year":2019,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"CancerCare Manitoba; University of Manitoba","funders":"","keywords":"Imaging phantom; Computer science; Reproducibility; Radiation therapy; Computer vision; Artificial intelligence; Ground truth; Nuclear medicine; Mathematics; Medicine; Radiology; Statistics","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":[0.001965485,0.0005642077,0.0002999344,0.0007008447,0.0002055279,0.0006403856,0.0008435589,0.0006054199,0.001294599],"category_scores_gemma":[0.004958399,0.0002746785,0.0003130429,0.0004618242,0.0003233649,0.0006657309,0.0004846634,0.0003504666,0.0001957626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006252963,"about_ca_system_score_gemma":0.0007774011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002148826,"about_ca_topic_score_gemma":0.001839191,"domain_scores_codex":[0.9994511,0.0001468528,0.00002911826,0.0001098618,0.0002068372,0.00005632511],"domain_scores_gemma":[0.9984573,0.0007126263,0.0002795208,0.0001206189,0.000357062,0.00007290928],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001565229,0.0006230806,0.005141565,0.0005765792,0.00007902005,0.00009428796,0.0003216264,0.07496597,0.5494548,0.001644974,0.0009620831,0.3645707],"study_design_scores_gemma":[0.0001367516,0.004729513,0.01073334,0.00005948016,0.0001570222,0.0003495112,0.00009283704,0.5430192,0.4370098,0.000397505,0.003199279,0.0001157295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.351588,0.0008862948,0.6429785,0.0002455116,0.000124723,0.0006078964,0.0001035168,0.001959289,0.00150621],"genre_scores_gemma":[0.6880729,0.0002935311,0.3101489,0.00008409053,0.00002531327,0.0001867371,0.00007689619,0.0001377593,0.0009737654],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002148826,"threshold_uncertainty_score":0.01039457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02874289574653309,"score_gpt":0.3503213978263506,"score_spread":0.3215785020798175,"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."}}