{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004603837,0.0002166597,0.0002558908,0.0005104401,0.0003526845,0.001156154,0.0003137815,0.001071527,0.002172949],"category_scores_gemma":[0.05657358,0.00018918,0.000277294,0.0003111004,0.0004751952,0.001164282,0.0004216215,0.0006925582,0.0006084838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005822731,"about_ca_system_score_gemma":0.0008863288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003254184,"about_ca_topic_score_gemma":0.003630078,"domain_scores_codex":[0.9975259,0.001206026,0.0002618915,0.0001735096,0.0006448576,0.0001879224],"domain_scores_gemma":[0.9640204,0.01912521,0.01265018,0.0007357744,0.002407118,0.001061291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001717522,0.0001415775,0.9730136,0.0001008825,0.0000346481,0.00007923318,0.00126205,0.0002992411,0.0001671994,0.00006908988,0.0007038259,0.0239569],"study_design_scores_gemma":[0.00002733712,0.0004722896,0.9909081,0.0002582428,0.00005105127,0.0006045154,0.002408858,0.002469924,0.0004451674,0.0004765582,0.001850849,0.00002712787],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895256,0.001190111,0.001361922,0.003636837,0.00004292,0.00004338909,0.0003728723,0.00003219008,0.003794092],"genre_scores_gemma":[0.9984975,0.0002359325,0.0008196243,0.0001567255,0.00002613651,0.00001505173,0.0001097507,0.000001363813,0.0001378864],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004603837,"threshold_uncertainty_score":0.02434766,"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."}}