{"id":"W1992845372","doi":"10.1016/j.media.2013.04.008","title":"The impact of registration accuracy on imaging validation study design: A novel statistical power calculation","year":2013,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lawson Health Research Institute; Western University","funders":"","keywords":"Statistical power; Context (archaeology); Modalities; Computer science; Image registration; Medical imaging; Statistical model; Statistical hypothesis testing; Monte Carlo method; Power (physics); Artificial intelligence; Medical physics; Statistics; Mathematics; Image (mathematics); Medicine","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":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.3536951,0.001824653,0.003258038,0.002340591,0.001498977,0.004204974,0.004651523,0.004560552,0.004098448],"category_scores_gemma":[0.6101632,0.001526686,0.004473136,0.003271769,0.005719606,0.004777507,0.004060274,0.004335135,0.0007414611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001413471,"about_ca_system_score_gemma":0.002423822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005778992,"about_ca_topic_score_gemma":0.0006204557,"domain_scores_codex":[0.6006821,0.3277777,0.02172856,0.02196007,0.02642996,0.001421662],"domain_scores_gemma":[0.2121997,0.708143,0.01664275,0.0491984,0.01304922,0.0007669552],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01911994,0.001171167,0.1199084,0.004315678,0.0221668,0.001330403,0.004963276,0.04184464,0.01854176,0.09205211,0.009026652,0.6655592],"study_design_scores_gemma":[0.007390053,0.01876207,0.1167345,0.001709338,0.02614194,0.004761885,0.000616742,0.6317981,0.03892846,0.1142279,0.03823115,0.0006979358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02515198,0.001344358,0.9680642,0.0008212022,0.0004896101,0.001649421,0.0001229194,0.000433558,0.001922854],"genre_scores_gemma":[0.6127353,0.0003993679,0.3773842,0.001188699,0.000464395,0.005403462,0.0001647886,0.0006120522,0.001647797],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6463049,"threshold_uncertainty_score":0.797009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0177570233426761,"score_gpt":0.3701413791481233,"score_spread":0.3523843558054472,"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."}}