{"id":"W2161596000","doi":"10.1093/fampra/cms004","title":"Can physicians accurately predict which patients will lose weight, improve nutrition and increase physical activity?","year":2012,"lang":"en","type":"article","venue":"Family Practice","topic":"Obesity and Health Practices","field":"Health Professions","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trinity Western University; Western University","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Cancer Institute; National Heart, Lung, and Blood Institute; U.S. Department of Veterans Affairs","keywords":"Medicine; Outcome (game theory); Weight loss; Physical activity; Confidence interval; Family medicine; MEDLINE; Physical therapy; Obesity; Internal 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.001243517,0.0003286582,0.0004491951,0.0001103056,0.001449619,0.00004650691,0.0001815409,0.0003445491,0.00005728565],"category_scores_gemma":[0.003063602,0.0003131454,0.00005654326,0.0003953782,0.00008368579,0.007160314,0.0002364706,0.001898143,0.0006088334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002575835,"about_ca_system_score_gemma":0.0003732113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002812612,"about_ca_topic_score_gemma":0.0002825372,"domain_scores_codex":[0.9951195,0.002360536,0.0003968984,0.0004760912,0.0005597907,0.001087256],"domain_scores_gemma":[0.9925395,0.004861141,0.000958032,0.0004554196,0.0006656026,0.0005203038],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01440863,0.07634087,0.3618716,0.01415621,0.001086637,0.00006217554,0.0826511,0.000003285552,0.01492216,0.01214741,0.2254667,0.1968833],"study_design_scores_gemma":[0.004342205,0.0006082661,0.5563325,0.0005012173,0.0004949587,0.000004185481,0.007302516,0.0002732407,0.0002148363,0.0003047803,0.4290556,0.0005656492],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9682851,0.0003927764,0.00004553323,0.008044537,0.001504486,0.001866568,0.0002793205,0.0001701278,0.01941155],"genre_scores_gemma":[0.9884298,0.001183972,0.0004388135,0.005912161,0.002922015,0.0003420282,0.00008577411,0.00005649805,0.0006288993],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2035889,"threshold_uncertainty_score":0.9999321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05468390330590951,"score_gpt":0.4130089978669136,"score_spread":0.3583250945610041,"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."}}