{"id":"W4206661354","doi":"10.31234/osf.io/gq7az","title":"Logistic versus linear regression-based Reliable Change Index: implications for clinical studies with diverse sample sizes","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; BioClinica; F. Hoffmann-La Roche; University of Southern California; Biogen; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Logistic regression; Statistics; Linear regression; Index (typography); Sample size determination; Mathematics; Econometrics; Sample (material); Computer science","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":[0.3657439,0.001938311,0.004509131,0.004608176,0.001679969,0.00548909,0.005534915,0.00354769,0.007842688],"category_scores_gemma":[0.851244,0.001285845,0.004582039,0.007070933,0.006171676,0.008703251,0.00442925,0.009049464,0.001627647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00271671,"about_ca_system_score_gemma":0.003583211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005585416,"about_ca_topic_score_gemma":0.003716456,"domain_scores_codex":[0.7032349,0.2460035,0.0139193,0.01938363,0.01603778,0.001420907],"domain_scores_gemma":[0.1268661,0.8115984,0.01981677,0.02397216,0.01632813,0.001418421],"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":[0.01415475,0.0007593756,0.2398799,0.007298152,0.01537193,0.001318062,0.006066269,0.04026625,0.001484233,0.06814238,0.08682776,0.5184309],"study_design_scores_gemma":[0.004141962,0.005976588,0.1493673,0.008915422,0.007389466,0.003832863,0.003535644,0.3369715,0.005196562,0.3914869,0.0821049,0.001080796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1087264,0.04806675,0.7437845,0.05440174,0.005838837,0.005301988,0.01001648,0.003543291,0.02032008],"genre_scores_gemma":[0.6185433,0.002788168,0.3545664,0.009528589,0.001700891,0.007670732,0.002101387,0.001421764,0.001678878],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6342561,"threshold_uncertainty_score":0.7821507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4653139843186258,"score_gpt":0.5314139307068233,"score_spread":0.06609994638819755,"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."}}