{"id":"W3042619146","doi":"10.1016/j.media.2020.101792","title":"Handling confounding variables in statistical shape analysis - application to cardiac remodelling","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Agència de Gestió d'Ajuts Universitaris i de Recerca; Horizon 2020; Instituto de Salud Carlos III; Ministerio de Economía y Competitividad; “la Caixa” Foundation","keywords":"Confounding; Robustness (evolution); Statistics; Medicine; Computer science; Artificial intelligence; Internal medicine; Mathematics; Biology","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":[0.02013602,0.001353163,0.0017446,0.001994454,0.001468889,0.002667839,0.002533602,0.002033862,0.003431626],"category_scores_gemma":[0.06117803,0.001122959,0.002695643,0.002116458,0.001725054,0.001271547,0.003556556,0.003172402,0.00148274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004392609,"about_ca_system_score_gemma":0.002721442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003887202,"about_ca_topic_score_gemma":0.00534191,"domain_scores_codex":[0.9938003,0.003500276,0.0004719083,0.0006443981,0.001388847,0.0001942857],"domain_scores_gemma":[0.9693444,0.02233305,0.001191818,0.003854579,0.002756744,0.0005193296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005904766,0.0002087169,0.01872433,0.0005377827,0.0007732827,0.0008513978,0.0008361618,0.0716145,0.02610608,0.01652363,0.006367766,0.8568658],"study_design_scores_gemma":[0.00007965416,0.0002107169,0.005738145,0.00006384986,0.0001784238,0.0009864863,0.0001433515,0.9348279,0.01617796,0.0330843,0.008438721,0.00007049253],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003429148,0.00008490405,0.9948305,0.0001063182,0.00003413512,0.00004311242,0.00005551557,0.001324429,0.00009195598],"genre_scores_gemma":[0.06790121,0.000190612,0.9293579,0.0001282037,0.00009113519,0.0001637835,0.0002156408,0.001073705,0.0008778732],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02013602,"threshold_uncertainty_score":0.1064907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01467764411556811,"score_gpt":0.2970592467573982,"score_spread":0.2823816026418301,"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."}}