{"id":"W2027556349","doi":"10.1007/s11548-006-0044-6","title":"Model for defining and reporting reference-based validation protocols in medical image processing","year":2006,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":62,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Computer science; Standardization; Terminology; Image processing; Segmentation; Reference model; Process (computing); Image registration; Data mining; Checklist; Data validation; Information retrieval; Artificial intelligence; Image (mathematics); Software engineering; Database","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002263678,0.0000878185,0.0004057532,0.0003155841,0.00004254425,0.00004637869,0.00006497149,0.0001060348,0.000004300142],"category_scores_gemma":[0.001124239,0.00006970064,0.00007417363,0.00005064172,0.0001041553,0.0001103005,0.00001849778,0.0003391248,7.39424e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003912036,"about_ca_system_score_gemma":0.0003624359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006739933,"about_ca_topic_score_gemma":0.000001370801,"domain_scores_codex":[0.9982874,0.00007412893,0.001099279,0.0001409005,0.0002703698,0.0001278972],"domain_scores_gemma":[0.9980549,0.000588238,0.0009456593,0.00004040958,0.0002914084,0.00007937732],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008509094,0.0002333663,0.6658019,0.0002232744,0.0001064486,0.001175548,0.0001216309,0.003330313,0.001403594,0.0004606925,0.001741157,0.3245511],"study_design_scores_gemma":[0.001518602,0.00005845872,0.1506499,0.0009325652,0.00001980657,0.004285171,0.000005589727,0.8413724,0.00005646585,0.0006713158,0.0003566131,0.00007313965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6143396,0.000154411,0.3801804,0.004761156,0.000144656,0.0003349812,0.000001010538,0.00001052277,0.00007329908],"genre_scores_gemma":[0.9395357,0.00001523632,0.05928478,0.0007315986,0.0003362204,0.00004955627,0.0000289783,0.0000091934,0.000008759961],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8380421,"threshold_uncertainty_score":0.284231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05690473528402875,"score_gpt":0.3747669587707829,"score_spread":0.3178622234867542,"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."}}