{"id":"W2472535515","doi":"10.1136/bmj.i3370","title":"A prescription for poverty","year":2016,"lang":"en","type":"article","venue":"BMJ","topic":"Primary Care and Health Outcomes","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medical prescription; Poverty; Computer science; Data science; Medicine; World Wide Web; Nursing; Economic growth; Economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002592751,0.0006089615,0.0009193369,0.0008701975,0.008389965,0.002286073,0.001606529,0.01669725,0.01489786],"category_scores_gemma":[0.02438024,0.0003992803,0.001048499,0.0006071288,0.003608385,0.003106682,0.003638015,0.03366356,0.002478995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003454115,"about_ca_system_score_gemma":0.01272961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06548277,"about_ca_topic_score_gemma":0.2372696,"domain_scores_codex":[0.9969888,0.000884235,0.0002802664,0.000245635,0.0009146836,0.00068646],"domain_scores_gemma":[0.9870368,0.004046776,0.0005447979,0.0003846282,0.00198689,0.006000141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002414428,0.0000889819,0.001150491,0.0001001549,0.00002213131,0.0004910285,0.0005971029,0.00001746116,0.0001049531,0.006685348,0.9720467,0.01867141],"study_design_scores_gemma":[0.00009827196,0.0001332212,0.003774615,0.0009432976,0.00005233684,0.0006333607,0.002454256,0.00007424709,0.0001113186,0.004705313,0.9869689,0.00005076043],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.001024889,0.004615482,0.0001377595,0.9685573,0.01664743,0.00002247456,0.0000555658,0.00003312721,0.008905964],"genre_scores_gemma":[0.01008529,0.004828427,0.0005147483,0.9358829,0.008185862,0.0000827574,0.00004934908,0.00003827859,0.04033244],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.06548277,"threshold_uncertainty_score":0.1302032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1021860930457474,"score_gpt":0.4744820223077655,"score_spread":0.372295929262018,"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."}}