{"id":"W3108711748","doi":"10.1038/s41366-020-00720-2","title":"Selection bias can creep into unselected cohorts and produce counterintuitive findings","year":2020,"lang":"en","type":"letter","venue":"International Journal of Obesity","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Counterintuitive; Selection bias; Selection (genetic algorithm); Medicine; Demography; Internal medicine; Computer science; Physics; Pathology; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003576601,0.0003372258,0.0006133111,0.0003606994,0.00007565707,0.0001434575,0.0005463656,0.0003261135,0.00009099202],"category_scores_gemma":[0.001838386,0.0003155382,0.0001432555,0.0001667414,0.0001241158,0.0003104772,0.0001417414,0.002084241,0.000004070595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006713095,"about_ca_system_score_gemma":0.0002444167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000758486,"about_ca_topic_score_gemma":0.00006584238,"domain_scores_codex":[0.9975459,0.00015101,0.0007572476,0.0003322131,0.0009943626,0.0002192784],"domain_scores_gemma":[0.9962485,0.0004692425,0.001075024,0.0001265184,0.00199124,0.00008945431],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001862113,0.0001348142,0.00637727,0.0002275626,0.0009472144,0.001848783,0.001318645,0.000001729344,0.003822333,0.00130145,0.9822223,0.00161172],"study_design_scores_gemma":[0.001276411,0.001592902,0.004813049,0.002382188,0.0005657909,0.005105372,0.0001146861,0.0001917067,0.06273051,0.6658384,0.2539139,0.001475114],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.3390675,0.0001402616,0.06610352,0.5901916,0.002256399,0.001030494,0.0001918825,0.0004337634,0.0005846014],"genre_scores_gemma":[0.733532,0.0005068019,0.05782067,0.1976356,0.009295987,0.00003529534,0.000197184,0.0002095168,0.0007669382],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7283084,"threshold_uncertainty_score":0.9999297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08674983111192126,"score_gpt":0.3661600039284307,"score_spread":0.2794101728165095,"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."}}