{"id":"W2042282848","doi":"10.1016/j.ijrobp.2007.07.081","title":"A Multi-Institution Deformable Registration Accuracy Study2","year":2007,"lang":"en","type":"article","venue":"International Journal of Radiation Oncology*Biology*Physics","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre","funders":"","keywords":"Medicine; DICOM; Voxel; Nuclear medicine; Prostate; Lung; Displacement (psychology); Image registration; Radiology; Image (mathematics); Artificial intelligence; Internal medicine; Computer science; Cancer","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":[0.00617036,0.001243783,0.001721476,0.003386418,0.0008561715,0.001660884,0.001468227,0.001575009,0.002795441],"category_scores_gemma":[0.01659165,0.001262427,0.00271526,0.003326533,0.0009312406,0.002077155,0.001132678,0.0009340009,0.002398981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009696379,"about_ca_system_score_gemma":0.0007359631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004232928,"about_ca_topic_score_gemma":0.005097683,"domain_scores_codex":[0.9952821,0.001274316,0.0007294576,0.001086119,0.001331797,0.0002961605],"domain_scores_gemma":[0.9784119,0.005407571,0.002320294,0.01045223,0.002956612,0.0004513145],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01776221,0.003973139,0.5380579,0.001209217,0.01058082,0.001436608,0.001970844,0.02274301,0.07495007,0.0006325476,0.005773732,0.3209098],"study_design_scores_gemma":[0.0008847753,0.006092045,0.849376,0.00009956818,0.008487835,0.0112528,0.0007458767,0.04569305,0.06971218,0.0005117286,0.006551117,0.0005931287],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9834709,0.001231411,0.01027205,0.00008724774,0.00004934366,0.0001520115,0.002139393,0.0006797749,0.001917865],"genre_scores_gemma":[0.9877886,0.0002642342,0.007997551,0.00004963569,0.00003253375,0.0000544168,0.002627395,0.000348331,0.0008373156],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00617036,"threshold_uncertainty_score":0.03263241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02542659822357966,"score_gpt":0.3781612521467613,"score_spread":0.3527346539231816,"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."}}