{"id":"W2566903824","doi":"10.1016/j.nicl.2016.12.018","title":"Selection bias in the reported performances of AD classification pipelines","year":2016,"lang":"en","type":"article","venue":"NeuroImage Clinical","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"FP7 Information and Communication Technologies; Seventh Framework Programme; Engineering and Physical Sciences Research Council; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; National Institutes of Health; Eisai; Servier; U.S. Department of Defense; Eli Lilly and Company; Lundbeckfonden; National Institute on Aging; National Institute for Health and Care Research; Pfizer; BioClinica; Biogen; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb Foundation; F. Hoffmann-La Roche; Merck; Alzheimer's Drug Discovery Foundation; University College London; IXICO; Takeda Pharmaceutical Company; AbbVie; Fujirebio Europe; Alzheimer's Association; Foundation for the National Institutes of Health; University College London Hospitals NHS Foundation Trust; GE Healthcare; Alzheimer's Disease Neuroimaging Initiative; Medical Research Council; Johnson and Johnson; Meso Scale Diagnostics","keywords":"Resampling; Pipeline (software); Selection bias; Pipeline transport; Computer science; Consistency (knowledge bases); Selection (genetic algorithm); Machine learning; Artificial intelligence; Sampling bias; Fraction (chemistry); Data mining; Statistics; Sample size determination; Engineering; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2162884,0.001547636,0.001903633,0.002152107,0.001458058,0.004536556,0.002516373,0.002458832,0.003278373],"category_scores_gemma":[0.4202885,0.001181123,0.002456358,0.002268258,0.003777957,0.003875442,0.003215336,0.002606634,0.001624373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002165362,"about_ca_system_score_gemma":0.002987951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00259923,"about_ca_topic_score_gemma":0.002182704,"domain_scores_codex":[0.8919181,0.07376095,0.007692202,0.01270405,0.01224467,0.0016801],"domain_scores_gemma":[0.5359434,0.3819393,0.01706602,0.04497798,0.01877646,0.001296953],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.01093321,0.0005575194,0.2338495,0.003209784,0.006496605,0.0003849093,0.003030733,0.09355362,0.02149711,0.02060032,0.01205163,0.5938351],"study_design_scores_gemma":[0.001781501,0.00746497,0.2515266,0.001131194,0.003912195,0.001989725,0.001215945,0.4415756,0.1153219,0.1425239,0.0307798,0.0007766787],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4183097,0.01076765,0.5454776,0.004430173,0.0006724971,0.002011801,0.003015868,0.004723272,0.01059153],"genre_scores_gemma":[0.8872737,0.0007165765,0.105778,0.0009239952,0.0001327189,0.0008225484,0.002269791,0.0006889685,0.00139365],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7837116,"threshold_uncertainty_score":0.9664559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3111626821699292,"score_gpt":0.4087706249836011,"score_spread":0.09760794281367186,"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."}}